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Direct Hydrocarbon Exploration and Gas Reservoir

Development Technology

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Direct Hydrocarbon Exploration and Gas Reservoir

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SUMMARY

"Direct Hydrocarbon Exploration and Gas Reservoir Development Technology"

In order to enhance the capability of petroleum exploration and

development techniques, three year project(1994 - 1997) was initiated on

the research of direct hydrocarbon exploration and gas reservoir

development. This project consists of four sub-projects. Three

f sub-projects are concerned with exploration technologies including

geochemical and geophysical methods.- The other project is focused on

the development of gas and/or gas-condensate reservoirs.

For the sub-project of oil to source rock correlation, the overview of

biomarker parameters which are applicable to hydrocarbon exploration

has been illustrated. Experimental analysis of saturated hydrocarbon and

biomarkers of the Pohang #E and #F core samples has been carried out.

Samples were extracted by stirring in dichrolomethane at 40-50°C for 10

hours. The saturated, aromatic and resin fractions of the extract were

obtained using thin layer chromatogram. The relative abundance of *

normal alkane fraction of the samples is low except lowest interval,

which is probably due to the biodegradation. The biomarker assemblage

of hopanoids and steranes has been characterized. According to the

analysis of saturated hydrocarbons and biomarkers, the sedimentary

environment of the Pohang core samples is marine and transitional zone

except the terrestrial environment of the lowest samples such as 610.5m

-9-

from #E core and 667.2m from #F core. The thermal maturity through

the studied intreval did not reach oil window even though slight

increase in thermal maturity with depth, which coincide with Rock Eval

pyrolysis data.

In order to check the validation of analysis of the biomarkers, same

samples were analyzed by the University of Louis Pasteur, France. The

distribution and relative peak area of the biomarkers were identical with

those by laboratory of KIGAM. For the 2nd stage of the research,

analysis of biomarkers other than hopanoids and steranes should be

continued:

For the part of the surface geochemistry & microbiological

study, the test results of the experimental device for extraction of

dissolved gases from water show that the device can be utilized

for ‘the g$s geochemistry of water. The device is capable of

determining, hydrocarbon gases in water to the concentration of

less than. 5x10H mien. According to the results of

microbiological studies, the plate count technique can be a useful

supplementary method for hydrocarbon exploration. This is based on the

facts that the average survival rate to hydrocarbons (pentane, hexane)

for heterotrophs is higher in the area known as containing considerable

hydrocarbon gases than other areas in the Pohang region. Howevr, it is

still necessary to develop techniques to treat the bacteria with gaseous

hydrocarbons.

In the study of numeical modeling of seismic wave propagation and

full waveform inversion, 3 individual sections are presented. The first

-10-

one is devoted to the inversion theory in general sense. In this section,

inversion schemes such as gradient method, Gauss Newton method, and

the full Newton method are reviewed to establish the new aspect of the

inversion theory on the basis of numerical tool such as finite-elements

in the frequency domain. The second and the third sections deal with

the frequency domain pseudo waveform inversion of seismic reflection

data and refraction data respectively. This inversion scheme is an

eclectic type inversion that lies between waveform inversion and

traveltime inversion. The forward response is generated in the frequency

domain as the pseudo waveform data obtained by low-pass filtering the

unit-amplitude delta-function data. - These data have phase shift

corresponding to traveltimes calculated by the shooting ray-tracing

technique. The Jacobian for a damped least square method is computed

analytically at each step of the forward ray tracing. The applicability

and feasibility of the proposed inversion algorithm is demonstrated with

prestack field data as well as with synthetic data generated by

finite-difference modeling. The estimated model of synthetic data

matches the true model closely, and the estimated model of prestack

field data provides a reasonable solution in comparison with nearby

sonic log data (reflection inversion) or in comparison with published

results (refraction inversion).

Due to the industrialization of the Asian countries including Korea,

the consumption of natural gas in this area will be drastically increased.

It is also anticipated that the consumption of natural gas will increase

more rapidly than oil consumption because of the environmental

-11

problems such as acid rain and green-house effect. It" is important to

participate on the gas development projects in foreign countries

including CIS to secure the stable supply of oil and gas.

In the study of gas reservoir development, the first year topics are

restricted on reservoir characterization. There are two types of reservoir

characterization. One is the reservoir formation characterization and the

other is the reservoir fluid characterization. For the reservoir formation

characterization, calculation of conditional simulation was compared with

that of unconditional simulation. The results of conditional simulation

has higher confidence level than the unconditional simulation because

conditional simulation considers the sample location as well as distance

correlation.

In the reservoir fluid characterization, phase behavior calculations

revedled that the component grouping is more important than the

increase of number of components. From the liquid volume fraction with

pressure drop, the phase behavior of reservoir fluid can be estimated.

The calculation results of fluid recombination, constant composition

expansion, and constant volume depletion are matched very well with

the experimental data. In swelling test of the reservoir fluid with lean

gas, the accuracy of dew point pressure forecast depends on the

component characterization.

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CONTENTS

PART 1. INTRODUCTION............................................................................. 21

PART 2. OIL(GAS) - SOURCE ROCK CORRELATIONTECHNIQUE.................................................................................... 27

Chapter 1. Generalities............................................................................... 291- 1. Definition of the biomarker............................................................. 29.1-2. Review of the biomarker parameters used for petroleum

exploration.................................................................... 311-2-1. Depositional environment & organic matter input............. 311-2-2. Thermal maturation..................................................................... 451-2-3. Biodegradation.................... 521-2-4. Correlation...................................................................................... 541- 2-5. Migration........................................................................................ 55

Chapter 2. Application of the biomarker analysis technology............... 572- 1 Separation and Extraction.................................................................. 572-2 Analysis of Saturated hydrocarbons............................................... 612-3 Analysis of biomarkers....................................................................... 642-4 Result and Discussion .......................................................................... 67

2- 4-1. Sedimentary Environment.......................................................... 672-4-2. Stage of Thermal maturity....................................................... 772-4-3. Discussion...................................................................................... 79

PART 3. STUDY ON SURFACE GEOCHEMISTRY AND MICROBIOLOGY FOR HYDROCARBON EXPLORATION.............. 8l

Chapter 1. Review of previous studies........................................................ 831-1. Vertical migration of hydrocarbon................................................ 841-2. Microbiological exploration ........................................................... 871-2-1. Bacteria............................................................................................ 911-2-3. Chimney........................................................................................... 931-2-4. Biogenic methane.......................................................................... 95

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1-2-5. Halos.......... _•..................................................................................... 971-3. Onshore hydrocarbon analysis........................................................... 98

1-3-1. Soil-air hydrocarbon analysis.................................................... 981-3-2. Soil-sorbed hydrocarbon analysis............................................. 991-3-3. Soil-occluded hydrocarbon analysis......................................... 991- 3-4. Integrative absorbtion............................................. .................... 100

1- 4. Offshore hydrocarbon analysis.......................................................... 101Chapter 2. Selected method............................................................................. 103

2- 1. Device for gas extraction from sea water..................................... 1032- 1-1. Component...................................................................................... 1052- 1-2. Operation......................................................................................... 105

2- 2. Test operation........................................................................................ 106Chapter 3.' Microbiologic study....................................................................... 107

3- 1. Materials methods................................................................................. Ill3- 1-1. Sampling.......................................................................................... Ill3-1-2. Methods........................................................................................... 113

3-2. Results...................................................................................................... 1133-2-1.. Determination of concentration for heavy metal ions and

hydrocarbon treatment .............................................................. 1133-2-2; Average number of heterotrophic bacteria

in Pohang area.............................................................................. 1173-2-3. Survival rate for heavy metal ions and hydrocarbons

at site A......................................................................................... 1173-2-4. Survival rate for heavy metal ions and hydrocarbons

at site B.........................................................................:............... 1233-2-5. Survival rate for heavy metal ions and hydrocarbons

at site C.................................................................. 1283-2-6. Survival rate for heavy metal ions and hydrocarbons

at site D................................................................................*........ 128Chapter 4. Conclusions..................................................................................... 136

PART 4. DEVELOPMENT OF GAS AND GAS-CONDENSATERESERVOIRS............................................................................ -139

Chapter 1. Introduction...................................................................................... 141Chapter 2. Reservoir characterization.............................................................144

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2-1. General characters of reservoir....................................................... 1442-2. Stochastic model.................................................................................. 1482-3. Model type............................................................................................ 1522- 4. Application of stochastic model....................................................... 153

Chapter 3. Characteristics of reservoir fluids.............................................1603- 1. Composition of reservoir fluid........................................................... 1603-2. Classification of reservoir fluid......................................................... 1643-3. Sampling of reservoir fluid.................................................................. 1743- 4. Analysis of reservoir fluid................................................................... 180

Chapter 4. Phase behavior of reservoir fluids............................................1834- 1. Fluid recombination................................................................................ 1834-2. Constant composition expansion test................................................ 1854-3. Constant volume depletion test........................................................... 1924-4. Swelling test of reservoir fluid .with lean gas............................... igg

PART 5. NUMERICAL MODELING OF SEISMIC WAVEPROPAGATION AND FULL WAVEFORM INVERSION.....2Q3

Chapter 1. Seismic inversion theory............................................................. 2051-1. Introduction............................................................................................. 2051-2. Finite-element and finite-difference formulation of wave

equation ................................................................................................. 2061-3. Inversion algorithm............................................................................... 2071-4. The Gradient method........................................................................... 2091-5. The Gauss Newton method................................................................2161-6. The full Newton method.....................................................................2261- 7. Efficient calculation of Hessian matrix........................................... 230

Chapter 2. Pseudo waveform inversion of reflection seismogramsin the frequency domain............................................................. 234

2- 1. Introduction....................................... 2342-2. Theory...................................................................................................... 236

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2-2-1. Damped least square method................................................. 2362-2-2. Parameterization and Forward calculation............................ 2372-2-3. Calculation of Analytical Derivatives...................................... 2402-2-4. The frequency band characteristic of the objective

function................................ 2442-3. Examples................................................................................................ 249

2-3-1. Synthetic data............................................................................... 2492- 3-2. Field data....................................................................................... 260

2- 4. Discussion.............................................................................................. 266Chapter 3. Inversion of seismic refraction data in the frequency

domain using ray tracing........................................................... 2683- 1. Introduction............................................................................................ 2683-2. Theory.......................................................................................... 2693-3. Examples................................................................................................ 277

3- 3-1. Synthetic data............................................................................... 2773-3-2. Field data....................................................................................... 284

3^4. Discussion .................................. 290

PART 6. CONCLUSION ...................................................................... 291

APPENDIX ......................................................................................................... 317

—16—

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Gauthier- *, 1986; Mora, 1987; Pratt *, 1990). Gauss Newton# * Shin(l988-)4 9494 4 44 #955## 4*94 *4449# 9 4999= $1-4 4*4#54, Full Newton#4 *444944 4# *99 Santos a *(1987)4 494 49* 4 $1*. “4 4 94#, 7># 5-99#: * *### 4*9 4*49"99 9*449 494 37> 4 49##„*94 *94# 4*94 *9455 5*944.

*44 *414 944 4444 49 5 49# 499 559 9**59 *5 Dix *44 44 14*4. 544 4 *#* 91 957} 4*9 54449 4*4 94194, 4# 449 49# 4 49 *52.14 914 59*4. 49 49## 4949 44 44 9*9-54 49 15* *1144 1494 199 4 *9 91# 7>l5 *4. 544 *9494 91*4, *14*4 49

-24-

# 7l* ## o]B|^ *^44*## ^.^7] *

4# ZlltM^M 33#* #4 #4 4 $14. #44*4 ## *4#44# 4*** tiU>^A] ^Xj-^d. 4^^ ^A]o^ 3# * **4# 2:7lxld)l **44 4*4 ##4#** *4 4 $14. "44# 4# 4#* ###44444 #4## 4 ###"4 4# #4f 43 5. 4444 4#4# 4444 4444 4**4 *** 4^#^ 4*33*4 #**34 44 43# *4433 44# 4 $14 #44*44 444 444 44 * 7>4 44^4 44^44 *4*#4 4*4(pseudo waveform inversion)# 7H #444- 44 #44 444 444 444 44^44 444^44 #4 4444 **43 $14. "#*#4* 4-§-4 *#*4 4 4714 ##4*34** #"44444 44 #444 44 4#* 4 #33 #44*44 ^

44 444 3144 #44*44 444 4444 4 4 444 44 44 44# *#44 #4# 4 444 4 #44 444# 7^#44 4. 444 *44 #44 ^ #44 *a 4*44 &## 444 7l *4 #*#*^33 #4 #4*4 4 *3# 4*4 # 4# # 44 3*4 e4 4# "SHI- **#.

4# *44 4471-43. #4 ##3 4#4#3 #4# 44-41 44 4*4 444 4##*4, 444 #33 44 443# ## 44 **7>34 ^47} #7>* 4*44. #4 **7}34 #4* 3tiH tflal44 £33 *44* ##*4 #371- 4444 #<y4 4*44 #4 4 44 44 7}^b4 4 #7}# #7}Ai4d> 4#. s. 4 4 4 d}# til 5.4 44444 444 7} 3 #*## 31*433 ?H # 3*4* #4 # 34 4# 44 #34#.

7>3* a* *#71143*4 IE#*## A#*## JSfflcfl# 4

-25-

-§-44- 7] € 4 44 4—7] 4 xr-o]] 4 3.xr H 4 4 A] 444 4 4

4. 7>r^S] #B4^ 14 114 &H4 41 1144 42]4 ^344 4444. ^344 7}^ 4 4#44^4 4 4H H!4

4 44 iWBHS. 4 44 4 414 7]## 44.37]] l ilHS. 4 £4 7>^ 1 yÂ]]c,]E x]#&4 7H44#

444 141 Hi 4f-%#@4 fill! 41H5. @JBc7]#i itb 4 4# tI^î 244Hi4 AAî4 ##444 H7]#i i

44 441 i 144. 444 344Hi# 4 44 M ^ 147]# & €44 1444 14 14 1 BM 41& 44# 4144.

—26—

*ii 2 a jjgutt - mt

4] 14 7fl A

1-1. 4i*l| S7|#°| go|

■a-fr(7>i) - ^€4 tflti] 7]# 4% 444 7>^1 ZL5]jl 5)3-1- SE4 s|44#4 4# 44 44 4 #71]# 44 s}# 4 4-4-4 4 #44 4 4 5E7]# (biological marker, biomarker)& -£44 #4 4 4. m &4#4 4# ^@#4# 44% 47] ###& 44 47] 4# #4 4"# s’ 4 44(Fig. 2-1). 4*11 &7]#-cr 47]- 44 (molecular fossil) 4 4m #4 47] 4##4 44&7]#4 4#4 44 44444 4 444 44 4444 4444. 44 44 Z444 444 44 44 44 4# € 4 $1-^.4 444 444t1] 444 4^ 44. 444 444 %# 4 « &7]#4 444 7>^4 #44 #^H4 4# 444^44

44, 44 44 44 44 % 4# #4#4 #4# 44 4 #

OH OH Biogenesis

HO OH

Hopone In Sediment Biological configuration

Bacteriohopanetetrol In Prokaiyoflc Organism

X = CH3, C2H5, C3H7 C4H9.C5H11

MoretaneGeological configuration

Figure 2-1. Basic concept of the Biological markers: precursor of hopanoid is Bacteriohopanetetrol.

—29 —

$144 ^44 4444 4$4$ # # $14. 444 44 $4 #4 §f^ 544 4#$7l- 144$ 4# #441 4# 444 4#4$3. 45. 44 4 #4, 414441 4144 #4# -2-44.

441 $711-41 44 444 19364 Treibs7> 4 #44 a]3.5#e1 $4 4#(porphyrine) #4414 44 444 44- Eglinton 4 Calvin (1967) # 44 $71#-#- 444 44 (chemical fossil)°14 41$ 1969441 Speer 4 Whitehead4l 44 4414 4 4 (biological marker)$5 444 4441$4 Seifert4 Moldowan4l 4# 4 $4#(biomarker)44 #444 4 #4$ 44. 44 $7l## #4 4441 44 444 44-2.5. 44 4 4 1 4 4 #414 44 ^ft°J 4 #4# ##4# 444 4 4?-## $4 444^, 444 444^, 4 4 #-2.5. #4 4# 444 4 4- #$44. 444^ #4 44 $5.# 4# $54-5 $f# 4444 M4 $4 44 3.5.-4S. $44 (prep-LC)# 4-§-44 #4 #4 $1$ 44 $7l ##'#444 $4$ 4 4 7M4 $1#4 $ # 7}$$54-5$4$ / 4 $$45544 4- 444 7>4 #4 $44 714 44(Fig. 2-2).

GAS CHROMATOGRAPH MASS SPECTROMETER

ION SELECTION

SOURCECOLUMN

SEPARATION

VOLATILIZATION ION PRODUCTION

Figure 2-2: Schematic view of the Gas chromatography - Mass spectrometry.

—30—

1-2. ## 0|#44 ^4 57|# 4^4^

44 #44 44 5* #4 4*4 #44 *## 44 #4 4144 44 444 4444. 4, 44 54## #44 #44 e##4 44 4 4, #4 #*5# 4 4 *54, #44 ##, #44 ###, e#4* e #44 4til7> 7>#4jl, e#*4# #44 4# #4 #5# #44 °1 # #&* ** # 4 44.

44 #44 4444 447>4 44 54 #4 45 *7>4 A#4 4 4 *455 4tifl 4fe 44 4# #45. #55.5. 444 447>4 44

44* Jl44°> #4. 4* #4 Ts/Tm 4 44 445* 7># #4# 4 4544 441 54*44* 5# e# ##4 44 4^£ ##=# #4 4 #55.5. 4 ##55 #44 5# #4 1# #45# 4*4 4 414 #44 7] 4 4 #4.

1-2-1. 44 4# * e# 4# 44###44 #44 e# *# * 4# ##4 *4^4 #41

5.714 4#444 44 4#44. 4#44 ?H#4 4# ##7ii5#* * 4# #45711-4 ##471444 4-44 4444 ### 44#4 4# 44, 4# ##44# 444##7}# #44 44#(Fig. 2-3). 44 # 44, e# ## # 4# ##* #444 44* 454* #41 54# * #4#fe #44 ##. ■'

* 54544/44 4* lb5454*/*44*4# 71-5315*5.3.4-5# #4##5 44 *5

#4 ##455 *4 4444- *4-4*4*. 44 ##54 #4- #7> *

-31-

5MH5. ft#, ftlftlft 4 #4 44ft4JZ 44 ft 4= 1^-4 Pr/Ph >

1 41 44ft443. Pr/Ph < 141 ftftftl# 444ft #44

$14(Fig. 2-4). ftftftl 4 4 4#(phytol)-Er 4444 phytenicft4 431,

#45.4^45)<4 pristene0]s)Jl 444 =4^414 44-

14 1ftH7} ^-Er 4444 Pr/Ph4ft Jift4#' 4 4 4ft 4 4ft4

4 &5L, 45.4 4^4JE7> ^4-n- 44 14144 Pr/Ph«l7} 3.0444 1 1ftft444 4-441 #14 4-44 #ft 44^ <0.641 444ft

4 44 SSzK(hypersaline)ft4 # 4 4 44. Pr/Ph^l 7} 0.8 44 2.544

41 Pr/Phtilt- #4 J7ft4# #444ft 414.

OLEANANE r4<j$P HIGHER PLANT- ANGIOSPERM

DELTA, COALS

DINOSTERANE x^U- DINOFLAGELLATEFAIRLY UBIQUITOUSMARINE ENVIRONMENT

Figure 2-3. Oleanane and dinosterane can be used as source specific biomarkers.

-32

Prista ne

Phytol (Chlorophyll)

CH20H

Phytane

Figure 2-4. Pristane / Phytane ratio can be used to indicate the redox potential of the source sediment.

* 441

444 44#4 44-2 424 15. &44, 44 ^424 44

441 44 2 #4. i£lt (Csi-Css)^ 4442244^151(bacteriohopanetetrol) °N- 4^ 4#^M1 444b polyfunctional C35 2

4144 144442 443l4(Ourisson et al, 1979, 1984; Rohmer,

1987). 14 C35 I£M° 1 &b 44b #^o] 42

5 4144. 4^44 1-fHM 444b C31-C35 17ff (H), 21 /3(H), 22S

4 22R4 444 b 44444 4+4, 4-4 42(Eh)# 4444. C35-25.

24 7] q(homohopane index) b C35/XC31-C35) 25241 4441b 425

C33, C34, C35- 22244 m°l C31, C32 54 4444 44 44

4444 124(free oxygen) H4 7)4 17}H 444b 44 444

4 44 1&4 44dl 4444 441-44 44142244131514 aj-sj.

44 C324 42 4-5H41 ^7ll 44 C3i4 44 4 C3i4 144 44 b oxic44 suboxic#4°1 42 Cs27> 1414 4b suboxic lb dysoxic442S. lb# 1 $14. 22244 b3Eb 14 4424 444

-33-

144 SLS-SL^: 41 [Css/CC3i+Cs5)homohopane]^ 44^7} #7}44 4

4 #4 #4 44 #3 1*44 14 44^4 441 jz.444 14.

* iLE^I ^.S^KBotryococcane, Fig. 2-5)

C34-°l^—4^4— H#4 4 4 4 3-7] H botryococcene^.5. *4

14442. 41 H4/H 2:1114 BotryobraunivQ^ 4145. *E]

7] H 444(Fig. 2-5). 44 If #4 (Botryococcus braunii in blackish

or lacustrine environment)* 4 4 4—5. #4 1*4 41 114 44] 5.

7]#&4 m/z 183, 238, 239, 294, 295 & 4144 4*44.

Botryococcene

Botyococcane

Figure 2-5?. ■ Botryococcane originated from the botryococcene in Botryococcus Braunii.

* *44K01eanane, Fig. 2-6) / C30 2.4 it (oleanane index)

m/z 191* 4144 4444 4444 zis.4 ^14* 11 -$-44

—34—

4 5#4# -n't](higher plant input) 4 # 4^ $14. #5))###

betulin (Grantham et al, 1963)44 4# 4 44 #4 -2.44 54444-2.

5^f£1 (Whitehead, 1973, 1974, ten Haven & Rullkotter, 1988)44€ 44

4. s# 44 4## 44-71 4:7144 44 # 42.5 «i00m.y) 45#

444 444 5IH4# #5)14-#4 #444 &##.

4 4 4-4 4 &51144 44# 4-8-# 4 18g(H)-#4## 44

18/3 (H)—141444 #t ##44 #2.55 ### 7}4545 #4. 4 # 4# # 18/3 (H)-#5)i4\+o) #44# #4455 4#4 4s.44# 7)4 4444 ###. 54# 4144 -2.## 444 444# #5)14# 4## 4-8-# 4 18a(H)-#4##4 18y9(H)-#5)14-# 5.## 4 #41# #4. 445. 4#4 45.4- 444 #4#4# 5##444 #5)1 4-## 444# 4# #4# #44 4455 ##4 44 #44.

#44# 44# 4#44 44 #44 5## 544# #44 #4 4### #4# 7># # 4 ## 4444# 4#4# 44 ##4 45 ir^ll### C35-555# 4#4#4 #5)1 ##4 Cso-^44 4## (44 7l#) 4444 4#4# 44 ##.

Oleanane

Figure 2-6. Oleanane and its fragmentation patterns.

-35-

*5)1444 17ff(H)-544 **<>1 &3l4#<q 7$*-

*444 m/z 4124 m/z 3974 2)37} 17 a (H)-5454 44 37)1 44

44. 3LB1JL S4* m/z 3697> 4711 444* 3] til§H *5)144*

ZL 43.7} $14. 5# *5)1444 259, 274 43.7} 44444 C30 3

4* 3^4 4444 444.

* 44*1^(Gammacerane, Fig. 2-7) 4*

@#%K(hypersaline)#/0 °f| 4 4444 41411571 #3. m/z 191, 412# °1

#414 4 #44. 4444* Cso-triterpaneAS (Fig. 2-7) ^57} ** §H

444 5*4 #4* 444* 44 571 #4 4. # cl 57} *7}444

Pr/Ph 4* 4443, 4-444 4** *444. iff~Carotane4

carotenoid4 44 4*44 4* 444 ##4 554 4*414 4 #44.

54 §1144 44^4°14 #44 *44445 4444(Rohrback, 1983,

Moldowan 1985, Hello et al 1988 a,b, Moldowan et al, 1992)

1914

Gammacerane

Figure 2-7. Molecular structure of the Gammacerane.

* iff -7}5.4(Carotane, Fig. 2-8) 4 454 3°15(Carotenoids.)

36-

S.±$ ##4^4 444& m/z 558(M+) 11 125 5. 4*J44. 4

5# In# Hi ####7ll #3E# 4##5 ^##4 4 31 41##1 °14 144 5# 441&& 1*1] 44. /3"4^4 4 C^-dicyclanel ^7] 4(anoxic) 44 #4 &1# 514 ##1 #444 (Hall and Douglas, 1983; Jiang & Fowler, 1986 ; Irwin and Meyer, 1990; Fu Jiamo et al., 1990). 444 oll-g: 14 #47141 54 4 4 144#7^ 14114.

b-Carotane

Figure 2-8. Molecular structure of the (3-Carotane.

* HI H4(tricyclic terpanes, cheilanthanes,) / 17 a (H)-Jl4 it

414 14-4#4 441 6.5. 44431 m/z 1911 l-g-sfli 4t11 4

#°] 7H44- °]1 4# 144 algal-lipids (tricyclic) 4 144 4##1

(prokaryotic) 4 Khopane)# 141 til IH.41 4°14 A/B4 41# A#

#441 41 Cas4 C29 tricyclohexaprenane# 1## 22R4 22S, (29,

30-bisnortricyclohexaprenane ZL2\3L 30-nortricyclohexaprenane) °]jz. B1

C29-C33 17ff(H)-s46l 44.

13/3(H), 14a (H)-cheilanthanes°143Z.H. 1411 114 ##4(Fig.

2-9)1 Cig## C3o4di4 e^l-H 43L #11# 5444 ti)^.### 1 144 (Seifert et al., 1978, Aquino Neto et al., 1983) 45# 1 C-13,

-37-

C—14 44 (0 a, a a, a0, 0 0) 47H4 4447}- #7l|5}# 4 444

5-1144# 0 a, a a7} #444 14:E7} #7}#4 44 0 a 414

4 4-447} #444.

Cheilanthanes

Figure 2-9. Molecular structure of the Cheilanthanes.

C30 444 Tricyclic teipane# prokaryotic membrane #44

^-AS/Qurisson et al., 1982) JL C30 444 tricyclic teipane4 14

#44.trucyclohexaprenolS. #144. S# TasmaniteS. 4 #4#4 4

4#H 7}444 (Colkman et al.,1988, Aquino Neto et al., 1989) $14".

Tricyclic terpane# 4 #54 4 44 4471 4#4 4# #4# 4441

1# 414 44 4 4444 (Seifert & Moldowan, 1979, Palacas et al.,

1986) 3E4 4## 44#7} 4# 4# 44## 44431# Ifiltl

44 14 4314 444 (Peters et al, 1990)

* 444 441 (Tetracyclic terpanes)

17, 21-4 s 44#(Fig. 2-10)1 4#4 44# 4554 4144- 4#14 3E444 14. 4# 57H4 314# 4# M44 41# !##!

-38-

5 f # E jlbM 44- 444554 4^44-.44# 44/3:44 ##57} #7}#41 44 #7}#-^] 4# 5

#4 444# #4 44 4444 444# 4# 4444 54 4 44

#4 4# #«H 444 444 444 444 4444. C24 444 4

44 #444 4#444 n 4#4 44444 444444 #44 4

44# 444 7] 5 44(Palacas et al., 1984; Connan et al., 1986; Connan

and Dessort, 1987; Mann et al., 1987," Clark and Philp, 1989).

1 9 !◄■

C24 17,21- Secohopane

Figure 2-10. Molecular structure of the Secohopane.

* 44 54 44 / 17 a (H)-3:4#4# 4"^] # # (eukaryote)4 44 4# (prokaryote) 4 444# 444

# 455. 5El## m/z 217 # 4-§4)4, 3:44 4#4 m/z 1914 4# #4 4 #44. 44455. 5E] 4/3:447>(>1) 4 44# 444 44 4

44 54544 444 414 44#4 4#45 zz.# 445 544 4#

44 4# 44 #4714444- 4#4 #71 #(microbiological recycling)4

-39-

#4 (Tissot & Welte, 1984).

S4 3if 34= 43# 43 233 #

3 7] €4] 3# 3# 33 (041 7}##) #3^4. 3if ^44# Czz,

C28, C29 & a a (20S+20R) 242 a ,9 /?(20S+20R)3#32 23##

C29-C33 ##3"1K#' Csi.33 C3s44# 22S, 22R 4^1 2# 5.4) 2# 4 443= 44.

* C27-C28-C29 —3 4

.C27, C28, C29 243# 44 2S3-3 S344 43#4 43 43 3 43(ecosystem)# 3 4 44. 3# 3 4444# 3443 43# 34 3 ##44# 3144 243# 3#4 #4 2S# 234 4#31# 4

44# 4# 44 ##44 3 #4 2. 44 (Peters et 4, 1989)

—4 4# 3 #4 443 3#» #4 2S3 S3 44 4344 34# #4 '443 7>44 3## 3414-2-3. C2s4 43 #342. C294 43 # 3 #4. 3# 442 Cas 243# 4 #3 ##2#3 4444 34 4, if5:#(diatom), 2#4 ^(cocolithophores), 445.##(dinoflagellates)#°1 #

444 99471331 #34 33 4 44# 3-2.5. 3434 C2&/C29 243

3# ##2344 224 533 #333# 0.53432, ##234 4

##44 343# 0.433 0.733 4# #44 #4 43#3 244 4

#3# 0.7 343 44. ## 3# 4-54 345# 4# 3^3 4W3

' 443434 43, 33 43 #344 4#34 4#34.

* C3o/(C27 to C30) 243 it (C30-54 4 4#, Fig. 2-11)

41343 #4# #3# 4344. 4-desmethyl C30 2434 #3# 414=43 #4 #4 #43 34 35# 3#44 (Moldowan et al., 1985;

—40—

Peters et al., 1986). 4 ###4=- 24-n-3.&# (propyl)

24-n-5.5.:S #5)1—^1]# (Moldowan et al., 1990)4 444 $14- °1 44

#^cr Sfl'cH-S Chrysophyte Sarcinochry sidales4 4 4 A34 444 4

ss. s}]-^#4 #4 4444 4^4ss. 1^4 44

"rS $14. 24-n-propylcholestane4 -g-4-c: GCMSMS (nVz 414->217) 1r

-S.44.

21 7 ◄-

Cao Sterane

Figure' 2-11. Molecular structure of the C30 sterane.

C30/XC27 - C30) —4 4 4 &4 b:5!! 4*4/5.44 *8* 4 h44 7] 4 -n*7!

#4 414 44 #4 ^44 ^s.1- <£-§: $14. 4S444 5 444

Si 4^1^- C30 ^444 44 M44 #$1^4 4fe Cso^4#4 4^

4 #4 4 44 #7#7l 444^7] 4wSS 4444 (Moldowan et al.,

1985).

* 444^44(Fig. 2-12) / w ^44 it

44-4 444 £ 444 tH-4 4444s. 14 ^4^4 44 44s 44. m/z 2174 4444 444 7>^44.

—41 —

^7’ -

Diasterane

Figure 2-12. Molecular structure of the Diasterane.

^4 4461-^1 4 4 "F^M] 4-S.4 5-44 e.(montmorillonite)^ 4

5-4ir4 #4444 444 diasterene6] 44 (Rubinstein et al., 1975;

Sieskind eL al., 1979). diasterene-c- 4# diasterane0! 44 (rearranged

sterahes), 13/3, 17/3(H) 20S zz.^31 20R S4 13 a, 17/3(H) 20S zzb]jl

20R 44, 4.^4# ^^W(Fig. 2-13).

444-^44/^444 ti!^ ^4°! ^444 444 ^4 44^ 4444 44* f44#4 4-§-44(Mello et al., 1988b) ^4#4

444^4.4/^444 ti]ir°! #4 44 (m/z 217) 44444 45.44 °] 44 444 e444 4444. £4 444-^44/^44 47} #4 44 4S7} 444 4444 444 -^444 4444. 44 44#4 4 4 444444 4^444^ 444^44/^44 47} #4 44 44

-42-

I

Montmorlllonlte

Sa-Cholesterol Dlas1erene(13-1 7) Dlasterane

Figure 2-13. Diasteranes are belived to result from clay catalysis of the precursors (modified after Peters & Moldowan, 1993).

4#4 4# #44 4# (low pH), 4# (high Eh) #544 44 # 52

5. ## #4(Moldowan et al., 1992). 344 <5f5 542 (Seifert &

Moldowan, 1978)4 A3##47> 44 ## 5442 44 #24 4/2# #u]

7} ^4 4444.

* 4-44 24#

444 444 444 47> 4jl 44 54 24424 444 J=4 # 44 4 24 4 4-f 4444 444 4 #24 4444 44# 442-4 4 44 4444# #4 5 #4 2 4444. 444 444# 4-44 25

4# #5. 442f#4 4a-4# 24*44 444 422. 444 44

(Wolff et al. 1986). 444 3 442 Pavlova^-4 prymnesiophyte 4 2#

4(Volkman et al, 1990) Methylococcus capsulatus (Bird et al, 1971) 4

42 444 422 #44 #4.

* V/(V+Ni) 24 #

5S# 44#44 44, 44244 444 44442 #5 3242

3444 44 4442 444 34223342 #444. 4444 4 # 2444 4#4# 444 #4, 44-5-44 444 4444 (Lewan, 1984). an4=4 44#4 V/(V+Ni) 24447} #4 54 444 suboxic #444 4442 47} 44 54 #45 444 4444(Moldowan, 1986). 22444 #444# 4444(Vanadium)4 42 4#(Nickel) 2444 44422 4444.

—44—

1-2-2. 14 4<lE

114 444 14 ##11 44 &4#4 4144 121 ^

#1 44 14 1141 141 14-14 414 41 14-1- 4144 14 445=* 7>11 f 14. 41 11 41444 #1

41 #4^414 444 4144 5a 14a 17a 20R 14^14 4^-4 1 7l 4414 14-^s. 14-44 44- C-20, C-14, C17 4444 4144- 44 S-4 1441 444 44 mi 5a 14a 17a 20S #4^144 41 5LS.4 4 144 441 5a 149 179 20R 14^1^5. 14 44 44(Fig. 2-14). 4441 144 4144-4 44 14#4 4444 4 1 *414 1414 14 11 1711! 11 4 1 14. 1 415=1 7>l 411 4141 1^41 441 ail-444 41 414- 14.

* Ts / (Ts+Tm)

444 41 114 11 1 141 4451 #44 14 11 114

1 &41 44-44-s. nVz 1911 4144 414 7>i44(Fig. 2-15).

Figure 2-15. Estimation of thermal maturity using Ts/Tm.

-45-

Sr CHOLESTHtOL

6al4al7a 2 OR CHOLESLANE

5al4bl 7b 20R CHOLESTANE MOST STABLE FORM

Sal 4a 17a 20$ CHOIESTANE STABLE FORM

Figure 2fl4. Structural change and isomerization of the cholesterol during diagenesis and catagenesis.

-46-

13 13* 151 C27 17a(H)-eh]^^(trisnorhopane Tm)l

C27 18 ff(H)-5.el 5^55:4 n OW4 1357} 34 3-4355. Ts 3

14 1413(Seifet & Moldowan, 1978). Ts/(Ts+Tm) 41# (Ts/Tm

433lh 3) 3*s i# #41-14 43 13371 s 344 4430.5.

13 2:4 c 13 33s 443 34 54!- 334344. Ts/(Ts+Tm)

411 #3 345. 43# #4*14 13 Si: 344 7>3 3434 4

314.

* 22S / (22S+22R); 4343C31-35 l7ff(H)-M3 3# 2214 4# 3343 1441 4343c

(Ensminger et al., 1977) 4# 34 54 #3 333 435.4c 4^3 4 # #543 1445.5. 434 3s4 &4 3444 35.3 1345. 4 44 53-3 334344. 4, 31-4433 54# 22R 3445 434 334 41-34 34313 31331 344 22R, 22S5. 434.

mJz 191 543 3#3c C31-35 343 22R, 22s 434 31 I£l

443 34. 14355. C31 4# C32 JL5S43 22S/C22S+22R) 37} 4

54 54 J7 ZL 37} 0.50 - 0.5441 31333:71 4445 0.57 - 0.62

41 4 3133144 4# 355 1114.

22SA22S+22R) 541(Moretane)3 35 C31-C35 513 3*34 13

3*57} 17}#4 33 133c4 22S/(22S+22R)37} 0.443 1 313

3144 513 355. 1114.

* iff or -5.4 5 / o' /3 - Jll and /9 /? - JL4

33*44 27l 3*143 43445 1134. 314 313 17/9

(H), 21 /?(H)-54 (1/9)# 41 14334 5143c 1433 ^#4.

-47-

4* 0 0 cr-2-4 44 4 a /9-M-9.3. 444 4# 14 *4

44-

17 a (H), 21a(H)-5444 44 17flr(H), 21l(H)-M:4 ti]

* 144*57} *7}44 44 !£44 44* 4*44 1* 0.8* 44

4* 44 4*4 4444 -$-44 1# 0.15 44 0.05 44 4444

(Mackenzie et al., 1980; Seifert and Moldowan, 1980). 544/544# 4

4# 4*44 C29 44 &7] #* 4*444 (Seifert and Moldowan,

1980), C%,4 C30* *44 4* 44 £ 44(Mackenzie et al., 1980).

* 444 444 / 17a-54

4#4 444 / l7a(H)-544 4* 14 1*57} *7}44 444

*7>44 (Seifert & Moldowan, 1978). 1*57} 4144 44 45454

4 4444 44 5454 zz.^4 44 444 4 4 4(cheilanthanes)4£454 45455*4 ®4 *444 4555 444-$-5. 44 144

4. 444 *1#!4 4# £144 44# 444 44]4/17a(H)-lt

4 45-4 1*5# 7}*4* 4* 4* *4* 4*44 44.

* 20SA20S+20R) £44 4 4 Wig. 2-16)

m/z 2175. 1444 44444 4*444 **4 444444.

C29 5 a (H), 14a(H)-£44 44*4 2044 45 44444 4444 4

4) 4*44 44 20S /(20S+20R) 4*4 044 0.544 4444 (0.524

4 0.55* 444"41- Seifert and Moldowan, 1986, Fig. 28). 4#44

* 020 R *444 #44*4 4*4 4*44 4144 4144 R,

5 *44 44 4444 44 4 4** #44 4444 5*145 *

*44 4 *44(Mackenzie & Mckenzie, 1983; Beaumont et al., 1985;

-48-

Marzi and Rullkotter, 1992).

^45. °Wl:E C29-3B} sis) 20S/C20S+20R) 4 #4 1## ## ^

4 114, 4&4 #4-4 4H 4^ 4#14 14423 4441 ^#7> #4. m#zK(hypersaline) #44 3 #4! 4 *141- ##4

1 44 1414 14 #41 244.

C29aaa RSteraneC29aaa S Sterane

Figure 2-16. Isomerization of the C29 acta sterane at C-20 position.

* 0 0/(0 0 +a a) ; 244 4^4 [%/3 0, or 14,9(H), 17^(H)/(/9

0 + a a) 4# 441

4#1 4*1 #4444 2.4 4#44 rn/z 217# 4#44 ^444.

20S, 20R C29 4# 2444 C-14, c-17 4*1444 44441 ##34

44 /3 /?/(/? /3 + a a)4#4 0.74*1 (0.67 - 0.71=444-4, Seifert &

Moldowan, 1986) 4444- 41 445. #4#! 4 3.4 #41 4*1

44#3 20SA20S+20R) it2.4-1 ^^#44 14 314-2.5. 14 44

37> 4h4 1# *114 ##3 114 ##44-. C29 2444 ,9 j9/(/9

-49-

0 + a a)4 20S/C20S+20R)# #4i #€444 €#4

Mi if ##44(Seifert & Moldowan, 1986). #7lf #44 €# 4

Mii Mi5. M4 4 #4 444^.5. ##44 £4-4

fj^7} Mi 4 #4 4 4 4#£ S14.

*44- W^/CM + #4 #W) lb : TA/CMA+TA)44# 4 4 #44 #444 Mi ##4 4444& m/z 253, m/z

23i# 4 #44 c-2.i m m# ^4&4M ABC-31 i#4- €# 4# ABJii 4##4 ii):2.-i-;E 44 4i 44(Figr 2-17). TA/CMA+TA)# ##E7} #7}#4 44 044 1004 4#. 44 #7}#4. 4# 4#M 447} 44. 4, #44 TA-M5-

4h# 4#r4#&4 ifii 4#4# 4#4 44

C29 Monoatomatlc steroids C28 Trlaromatlc steroids

Figure 2-ITT Conversion of Czg-Monoaromatic to Cgg triaromatic steroids during thermal maturation (after Peters & Moldowan, 1993).

* 44 4## 431 lb : MA (I) / MA (I+n)#€#4i 44 4^ 44# #4# 444 m/z 253# 4#44 &

-50-

7l 44444 ^A 44444 ! 44s #44 444^ 4444014. 4^ 4## ^4s.o]h^ 1444 44^4 44 444 44 (Side

Chain scisson)4 444—5. (Seifert and Moldowan, 1978, Mackenzie et

al., 1981 a), MA (I )/MA (1+11)4 4vr 044 1004 fc 44

4 (Fig. 2-18).

Group II Monoaromatics Group I Monoaromatics

Figure 2-18. Conversion of Group II monoaromatic to Group I monoaromatic steroids (after Peters & Moldowan, 1993).

x

Group II Trlaromatlcs Group I Trlaromatlcs

Figure 2-19. Conversion of Group II triaromatic to Group I triaromatic steroids (after Peters & Moldowan, 1993).

-51

* #4 ### 24545 Jt : TA (I)./ TA (I +H)

e4#44 444 4## #4* 444 4*444 *4 44444 44# 4444-5. (Fig. 2-19), m/z 2314 4444 44#4. n44 I

55.4 #44 14444 44 444 &;H44 4^0 1444 44-5 4 4 #4.

1-2-3 ^8##4 (biodegradation)

444 s44 4444-4 444 4* 44# #sfl#* r^-?I—5 4

44JL 454 (Milner et al, 1977; Palmer 1984b) #4^ 4444 44

(45. sulfate reducer) 4##5# ##44#4 , #4 5 *57]- 41* —

1 #44. ^8# #41* 4* #4* 144-5.5 2=4 4*44 427}

#H4 44 4444(Fig. 2-20). ^4 54#* 2=4 44-4, 42545

45, 244 5:#, 444-^44, 4#* 24545, 5444 *55. ^

# *414 44 4 4(Chosson et al., 1992, Moldowan et al., 1992).

## #44 1441 *4* 4*4- 44.1) .. 2=4 4444 4-f #444 4* 44 #454 #4 ^*#314

4444.2) . 4 4 554 2=4 ^(extended isoprenoids)* -^8 ##41# #4 #5

5.4 2^14, 4444 zte]5 4# 455454* 4*4 ^84157] #4 -g- 444 444 #44 #4144.

3) . 2.44 4* #41* C27-32 > C33 > C34 > C35 17 of (H)-24*4 5

5444 4* a a or20R, a 0 j@20R>a a a20S, u- £ £20S, Czi > C28 > C29 > C30 *4 44.

4) . 5444 4* a a a 20R (C27-C29) > a a a20S(Czi) > a a a 20S (C27) > a a a 20S(C2s) > a a a 20S(Cz9) > a 0 0 (20S+20R) (C27-G29)

-52-

4 As. 444.5) . 4°14—^ 4-^ 44 C27>C28>C29T—s. A8# 4^0# 444-6) . 444 —^11 -S.°l —-^1 44 C20-21 TA > C21-C29 20R MA - C26-28

20R TA > C21, C22 MA 4AS. 4# 4^4 444 444

a) Before Biodegradation

Noimol olkone piedomlnont

b)Biodegradation Effect

Figure 2-20. Normal alkanes are removed due to the Biodegradation.

-53-

1-2-4. tflti]

#4 54#* 4 #4 4 *#4 1#, *#4 e*#, e€*4 *1# & 444* 4* #44 1*# 444 4444# 4#44 7}*44. * 454 44 *4, #€ #4 #1, 14 4*2, 4# #4 *24 °1#4* 444 44 541-01 4#, 4# 244 44 44# #4.

444 43-# 4# #4 54# 4444 442 444 ##* 44 44s. 54* 44 54## 444 44.

* Cz7-Ci8-C29 444244

7>4 4#4# 444444 #4 444 ##44. m/z 217# 4#4 4 444' 7>#44. 4# [C% 13/3, 17a(20S+20R) 444244] / [C27-C28-G29 13/3, 17 a (20S+20R) 444 24434 4* 44 44 #■&* 4#4- G28, C294 4442 4471-4 ##25 4444.

U 27; 28, 29 444244 44447} 7>4 ##44 24# 4# ## #47} #44 24425. #27} 43# ##4 ##27} ## 214 1 44455^4.23 44 7} 42 4442344 4# ##44.

* C27-G28-C29 C-24 41 #4# 24542

44 4.4445 ##44 244 44 ##4 #14 #442, m/z

253# #4. 4#4 7}#44. C24 #4# 24542# 271 4444

442 #44 47}4 4# 14# 4# 24#44 #41 1425.

(Riolo et ali, 1986; Moldowan & Fago, 1986) 24454 41# !##!

* 4444. C27-C28-C29 MA-Steroid #425* *44 44 #1###* 1# 14* **2 *44 144 (Moldowan et al„ 1985) 445 4

*1* 4*4 40J43 o_ 1* 41 4*4 4*4 2154 C29 412*

-54-

^45457} *Al]*51 ^71-^rgr Czi, C% 4*31.4 ^454^7} 7]*

*4- 2% ** 7] ■* ^1^-5] Cgs /(Czs+Cgg)^!^" 0.5# 44*4-

* nVz 191# 4## 44* *4 4444

44 **4 4*4* 44445. 4# 4^4 444 4

3L4 4# 4 444 44# 3*s 44. 4**4 44## 44^#4

444444 #44^(Ourisson et al., 1982) 4#4 ###4# 444,

4#4, #44, 4*4, 544(S4*)*4 #4*4. 44*4 4*44

m/z 1914 4* C3o5*/C295*4 44 154 #4 C29544 4 *4*

*#* *7] #4 **4 ##*, 4*44444 (Zumberge, 1984; Connan

et al 1986; Clark & Phip, 1989) #444 #44 4* 4## 44*4

(Brooks, 1986).

1-2-5. 4 #

4**4 ** 4*4 ^4 3.71*# *4*4 4# #4*4 « 3

4*4 *4*55.4 4*4 4* *5# 4* * * *4. 4, 4*7} #

44 4*# *4 #4*4 « 34## ** * #4 4*4 « 34

*4 *4* 4* *444 *4 ** *4# 444# 4*7} *4. 4#

4*7} 4*44 *** 44# 4# 4* *4 4*4 41*4 *44#

*#44. 4* 4*4 4*#54 4*4 #**4 444* 44 34*

# #4*4 €*4 441 34*4 44 *554 *## 444 #€*#

*4 # * *4(Fig. 2-21). 4*4 #*** 44*4* *44 44 *

** ** 4*11 34* 4444# 4* 44 **4 ## *5 4# 4*

#4-4# 44 (negative correlation) ** 4# *** 4 (positive

correlation) 5* 54444.

-55-

IMMATURE SOURCE ROCK

AAvAa

8 1CS §& §8

lUI k rJ\NO CORRELATION

C29 ooo ?0S. g

ft

SOURCE ROCK-Ol CORRELATION

C29ooa1

8uu w

Figure 2-21. .Biomarkers used for the correlation of oil-source rock and migration estimation.

-56-

*11 24 -8- -g*

^•is 19# #44 419 44 &9# 94 4 114 ?]#&41 44# 4s4 9 #44 9*4 14# 7js 9-83 #9 <994#1 99# 4s= 484. 44 #9# 94 994 999# 8£4911) 4 = 4^ 94 4^9 s 4444 #9 *15.1- 94, 9## 99 484.'

41 19# 1114 44 499# E9, F944 94 Is. 44 12, 117H 4si 98484. 914 4s9 9-4 H99, 49 =ls4£nl 4 44 9# 4 94 4^ji HP 5890 44s n 7}*3£4e zl4s4 HP 5989 A *%4ssil4# 49414 944499, 94 &9# 9 49 484. 944 941 29## 41444 94 444 44 #9, 19 44s# 9948S4 4# 4 #4 4s4 9h 4 £484.

2-1. 9*l# 9# 1 3E9 95-1

9441 44 4 s# E 9 12711 4s, F 9 11711 4si 9944 4 #1 4# 4 s9 94# 94 484. 491 4# 4 £4 949 Figure 2-22 4 19 944 44 494 84.

, 2-1-1. 991- 9#

#7l# 9# H# 49 3.7)1 97>4s. 494 494 7l7l# 4#4

9 114 44 31944 44914 4#49 114 84. 949soxhtec 44 soxhlet## 4944 9#4# 844.

-57-

Sediment

Extract: Magnetic stirrer: 10 Hr

Thin Layer Chromatogram

Saturated HC

Hydrocarbons + Resins

Aromatic HC

GC/MS GC

1Molecular Sieve [C / TLGureaAaaucnon

n-aikanes-Wv\

Mono aomafcs

Cyck) dkcnes

GC GC/MS GC/MS

Figure 2f22. Flow chart for bitumen extraction and fractionation.

-58-

*7]# 5*4 44* 54434- S}5 *7] *4 #44 444 ^* 4444. 5 4444 44 52* 5*# 4*4* 4ti£lM 4 -§-4 5*44 554 44 43i 44 44 52 44 45-44 #3* 4 5 #4 #4 44## #33. # 5*4* tssif 4 44#, *54# #47} 44 5*44.

5.4 444 44 #4# 4M4 4*4 5# 44# 4*44 4#4

44.® *4 444 4s. 50 3^* 100 - 150 44 3.43. 544 44.

© *4 45# 4#4 554 ¥3 24 44 #4 52, 55# 4444.

© 524 45# 4444 ¥3 Ajs. <$o] 2-344 4=4 444* 4 #3.3.44# 47> 44.

© 454 #47> *4 $1* 4 47i* 7>14 44 34444 ¥3

40-50°C2 4 12 44 45 44544 #4** 5*44.© #4# 5*4 #4 454 #4* 4* 44 4444 4454 4

44* 4*44 #44 #4*# 4444.© 5*4 *44 *4#5 #4* 44 **444 5# 44 *4*

# 4*4.

2-1-2. 54 *4

4*44 24 #4 M5 447>47> 4*4 54 454 55 5*4 4*4# 544*7] 444 4#3345zz.4(thin layer chromatogram,

Fig. 2-23)* 4*4^4. #444 4*4 24 *4 45* 4#4 44.® #44 44 3=7]] #44 4* 4444 *44# 4*44 45

* 54 44(Fig. 2-23). 44 544 4*4 **#4 42 7}^* 544

-59-

57.5.711 T€4(Fig. 2-23). S4 44* 4&* ™5l7] 4si ^7121M »M1S._0 5. ^7fl X\ ?1 Ck

SYRINGE .NHROGBJ SUPPLY

-UC PLATE

Figure 2-23. Schematic view of the Thin Layer Chromatogram(TLC).

© 444 44*0)1 515.711 4W 4#^ 4€* c>l-H14 £ 7H 2:4^1 ^7H 444. €*- €44 44 44*444. «fl^ol 444 ^°)1 id ^=4 444 ^44^#4 44* 01-B-4°1 444 44-t^ 4**5. *44-4 €44 €*#4 *4.

© #44 €44 4* #a&3E#4 444 ** 44 4444 44

* ^4 44 ** 4444 MM** #4 444.© 444 444^ 4#4 4711* 4 f *4* €444. -

—60—

4# 4# 444 4314 #44 ##44 5## Table 2-14 444 ■$14. 4 # 4# #### 4# #5-e?M 4# 444 44

4 4444 #^4. 44 Rock Eval 1 44 44 444 44 #4#4443# Tmax7> #54] 44 3.711 4444 &# 44 444 435.4 4 $14.

2-2. 54 B4 43 44 W 44

4514 54 E#, F#4 ^ 54 43H 7}5 3.545 345# a>#

44 54 4443 44 & 4 4 44 4( Appendix #5).E# 4 #;#4 54 4443 3545344 44# $1 #44 4#4

44. 4, 34 444 454 44 444 4)4 #34 C% 44 C3i4#4

4 34 444 431 5513444 44444 44 #31341 4# 4^#

(4# 4# 45 2-3) 44 435 544(Appendix #3). 34 444 4

4435 4-711 4444 444 4444 44 44 444 445 71-44

34 444 434 44 47> #71-44 610m #^<21 4444 44#4

34 444 45 444 4444(Appendix 4*5). E 44 120.6m,

250.3m, 518m, 560.3m #£#41# # 44#4 444 4444(Appendix

45). 5515314 44 3144 4# 429.4m, 370.7m, 570m, 610m#5# 31

443# 154 #4-4 #4 444 # # f}c}(Table 2-2). E#4 45.4

43 #31 4## 0.76 - 1.585 44 714 #711-4 4 #44 44457}

4-44 ## #35 4# 443 # # $134 4# 4# 43154# # 711-4 #5 4144471 4 #4 43 #41 4 #7} 43# #41 444 4

35 31# 44.

55153144 C17 34 44, 443144 Ci8 34 444 45# #7l

#4 4# 4# 4 #44 #41-# 4# 54 E # 43.4 tfl##4 3#

—61 —

Table 2-1. Quantity and composition of the soluble extract.

DEPTH(m)

TOCTOTAL

EXTRACTSAT ARO NSO SAT/ARO

120.6 2.75 135.06 29.55 5.11 65.34 5.78129.3 2.66 68.08 52.01 8.45 39.54 6.16250.3 2.63 130.6 36.4 8.75 54.85 4.16279.9 3.05 154.15 15.82 8.47 75.71 1.87290.1 2.96 70.1 25.09 7.82 67.09 3.21

ES 310.3 1.82 96.6 24.1 11.85 64.05 2.03370.7 1.80 46.85 36.73 8.54 54.73 4.3429.4 2.32 122.6 29.78 9.14 61.08 3.26518 1.56 75.11 52.32 14.73 32.95 3.55

560.3 2.08 121.06 41.03 12.95 46.02 3.17570 2.56 81.2 38.02 16.64 45.34 2.28

610.5 1.79 65.7 58.91 16.97 24.12 3.47110 2.66 123.54 33.38 9.20 57.42 3.63120 2.55 78.95 39.53 19.26 41.21 2.5

160.2 2.47 132.22 21.81 4.58 73.34 4.5210 2.45 59.18 41.42 10.37 48.21 4280 2.07 46.36 30.19 13.72 56.09 2.2

F5 351 2.23 87.11 33.13 16.41 50.46 2.02401.5 2.06 89.6 49.55 11.99 38.46 4.13489.4 1.73 99.1 34.8 12.58 52.62 2.77549.7 2.46 145 39.82 9.62 50.56 4.14599.8 3.71 431.32 66.32 6.81 26.87 9.74667.2 3.74 298.95 51.61 10.64 37.75 4.85

-62-

Table 2-2. Gas chromatography peak area and ratios for E core.

E 120.6m E 129.3m E250.3m E279.9m E290.1m E310m E370.7m E429.4m E518m E560.3m E570m E610mC15 - 21179 - - - - - - - - - -C16 4898 51175 - - - - 64393 33586 93261 38579 93261 -C17 11694 73438 12365 - 6988 - 62138 43215 91838 46443 91838 26811

Frisians 40089 95819 73400 23007 24210 - 170516 179687 177351 167042 177351 46499C18 10186 93099 11080 3058 8871 27464 71439 56281 82106 50882 82106 45106

Phytane 81878 169076 135766 56729 46706 176156 165322 176649 202762 173708 202762 39336C19 6576 69347 10739 3019 10394 67578 53987 46247 98337 45939 98337 70772C20 6092 53378 10421 3892 10354 106791 43648 39983 60052 42397 60052 77650C21 4606 31822 9291 12023 7583 83718 47143 26535 69197 38754 69197 101496C22 14201 21985 19223 12695 35981 334625 62679 55259 78848 54901 78848 96018C23 4341 12719 10301 4599 7424 68768 40552 33297 57410 37505 57410 126349C24 .2915 8473 8102 2650 6907 74497 44225 34031 68884 37840 68884 110333C25 6493 12529 9522 4112 10197 63057 39988 30400 73578 45367 73578 157010C26 3287 10097 7017 - 3936 22435 25019 20116 45789 36029 45789 134810C27 9895 27065 14285 7066 16344 80423 83891 71584 39550 32675 39550 187001C28 12313 22167 26884 11623 15932 135615 42005 78311 17586 15522 17586 106905C29 17693 49544 42409 12223 13493 183059 94496 80307 46931 54235 46931 157416G30 18164 30615 45275 18795 7298 246383 42922 120288 55497 46276 55497 123601C31 18626 37840 7733 5227 9819 120935 103819 132979 98053 68142 98053 123021C32 - ' - - - - - - - - 30079 - 66077

Pr/ph 0.49 0.57 0.54 0.41 0.52 0.00 1.03 1.02 0.87 0.96 1.10 1.18Pr/C17 3.43 1.30 5.94 - 3.46 - 2.74 4.16 1.93 3.60 2.94 1.73Ph/C18 8.04 1.82 ■ 12.25 18.55 5.27 6.41 2.31 3.14 2.47 3.41 2.44 0.87

CPI 1.11 1.52 1.03 1.27 0.98 0.76 1.58 0.98 1.09 1.21 1.44 1.36

4 ^#1 44 4! 455. 44454 44 429.4m 4 610m 451 4. °] tfl (transitional zone) 4 141 4444(Fig 2-24).

F# 45.4 35.# 44H HS4Sn^^ 145 E#4 44 41 1

/}#& 4444. 54 44 M4 #44# 5# #44 155 E#4 14-44 #1 ill 44 43# Cie 44 C3i4444 5# tiH] 441 54^444 44444 14N4 S# M#(S# #4 45 2-3) 4-4 4—S. 54 4(Appendix #5). 4 4-4 4 #5 E#4 4444 445. 7} 44 #4 #44 599.7m 44 4444# 5# 444 43.7} 4-44.03. 1444 4444. F#4 54 44 15314 4 44#4 444# 43.1 110m, 160.2m, 210m, 351m, 401.5m, 549.7m°l 4(Appendix #5). F #4 54531# 44 4 #4 4# 0.35 - 1.014 15# 4444 44 4^1 4444(Table 2-3). #5 4% 41* 34 Sir 4 0.95 - 2.154 Ml- 444#^] 4# #453. 444 44 #35 #143 444 1

4#4 44 #453. #4 44.=4^444 On ill #4, 44444 Ci8 3# #41 43*11 ! 4

4 F # 45.4 4!## S14 441 #4 11 453 4443. #4

# 444 441 44S4. FI 667.2m 45.4 41 54531## 41 1444 444# 44 44314! 44 4# 44 #44 14 44 4 14 14# 144 #1 41 4444. F 1 667.2 m # C29 aaa R 5

444 14 444 3% 131444 4, &44 5444 Jt*4 44 5.41 14 4-4-3145 44 14 44 441 4414.

2-3. #!4I 5711- 14

54 4i 45 E, F #44 444# 44 5411 slKhopanoid)

—64—

Prist

ane/

n-C

17

1E-1 1E+0 1E+1 1E+2

Phytane/n-C18

Figure 2-24. Estimation of organic matter input using Phytane/n-Cia Pristane/n-Cn ratio (zone 1: trenestrial, zone 2- transitional, zone 3: algal organic matter A : E core # : F core)

-65-

Table 2-3. Gas chromatography peak area and ratios for F core.

FI 10m F120m F160.2m F210m F280m F351m F401.5m F489.4m F549.7m F599.7m F667.2mC16 33953 '16775 - 28826 42690 52128 - 82013 - - -

C17 38530 15105 9266 37280 62055 98652 38257 75433 21616 36012 28477Pristane 117520 55493 45390 121002 222301 324688 135550 367808 125537 57971 86052

C18 32828 11265 10126 30431 83578 121534 37022 85772 25509 36854 52789Phytane 190006 66589 128456 153028 230308 389230 151187 362968 130791 165407 -

C19 39498 8563 9908 22885 47875 94504 41802 94368 20108 50746 85844"C20 28528 8871 8232 25886 63666 76807 45081 77288 26930 62826 102423C21 39715 8711 16853 30818 56642 62657 39235 52812 18447 53851 145893C22 52886 10966 22514 45178 87042 123910 64646 95362 31493 61390 204239C23 25740 10701 8382 26731 81603 80027 39869 83181 24810 74883 311506C24 20918 8813 6365 25590 68066 70016 47406 74746 24728 64898 327499C25 32069 15233 24911 34759 98436 81756 49481 127282 27056 93400 506342C26 33749 15276 - 14958 31757 51126 36689 56240 20726 67142 346950C27 63144 37471 13171 49725 167812 92338 112931 164215 58072 111248 433032

' C28 65264 22774 19442 28705 107601 179745 116211 165163 50491 67454 240661C29 99931 70169 26795 98082 300356 203737 225306 308710 61772 148562 455762C30 25830 19310 27800 35465 120782 233717 118483 259615 29684 76889 207686C31 29231 72296 15804 75745 274920 232674 176548 209477 69516 122516 573674

Pr/Ph 0.62 0.83 0.35 0.79 0.97 0.83 0.90 1.01 0.96 0.35 -

Pr/C17 3.05 3.67 4.90 3.25 3.58 3.29 3.54 4.88 5.81 1.61 3.02PIVC18 5.79 5.91 12.69 5.03 2.76 3.20 4.08 4.23 5.13 4.49 -

CPI 1.39 2.15 1.44 1.91 2.08 0.95 1.46 1.44 1.36 1.59 1.52

* *4 44 m/z 191 14 3)3-1- 4*4^4. 3*4 541 4

4 57)14 111 Ts ( 17a(H)-22,29,30 -trisnorhopane), Tm(18a(H)-22,

29,30-trisnomeohopane), 13(18) 54(hopene), C2gl4a2iP(H) 54, C^Pa

54, *s))44, CsoaP 54, 21) 4(moretane, CsoPa 54), C31 aP 52.54

(homohopane) 22R, C31 aP 5254(homohopane) 22S -F0H(Fig. 2-25).

5e) *l(steranes)# 3* #7] 44)4 m/z 217 14 3)31 °1*4 34

C27 Pet 4tiH 544 S, R, C27 ap 444 5e)* s, R, Cgg pa 444 5

44 S, C27 Pa 444 5e)* R, S, C% acta 5E)* R, C27 aPP 5E-)* S,

R, C29 Pa 444^44 R, S, C% ap 444^44 R, S, Cggapp 3E)* R,

S, C29 aPP 5e)* R, S, C29 aaa 344 R, S 14 5414 4*4%4(Fig 2-26).

54 444) 144-141 3.7] 1 144 434* 45 44 4#444 *44 4*4 451 545 14 4-342 444 4444 444

^ 34 *4, 44 57)1 44* 444(Fig. 2-27). 14 4-543 44 4144 44)4 4*4 3414- 34*14 *3 4 434 14)4 44

4 *1 14 41544 4* 44 44#* 344.

2-4. S4 W 5#

4*4 14 57)1 4444* 4*44 5# 44- 41434 44

43 4 14 *4 *4, *7)14 * 4 4144* *4 3&4(Tables

2- 4, 6).

2-4-1. 44 43 4444(Table 2-6)

5# 44 4*144 3*4 44 5414 4444 *** *444

—67—

Ion 191.00 (190.70 to 191.70): E560.DAbundance 55000H

50000 -

45000 -

40000 -

35000-

30000 -co o

25000 -

20000 -

15000 -

10000-

5000 -

100.00 102.00 104.00 106.0098.0096.0094.0092.00rime- -> 88.00 90.0086.0084.00

Figure 2-25. Distribution of hopanoid biomarkers, E core 560m.

Abundance 217 .70)Ion 217.00 (216 .70 F549.D

60000 -

50000 -

40000 -

30000 -to. tt>

$ 0)W CD

20000 - c 92£ <9

10000 -

94.00 96.0092.0090.0086.00 88.00rime--> 76.00 78.00 80.00 82.00 84.00

Figure 2-26. Distribution of sterane biomarkers, F core 549.7m.

Inte

nsity

F549.7m

a. %

Time

Figure 2-27. Distribution of steranes, F 549.7m analyzed at Univ. of Louis Pasteur organic geochemical laboratary. (compare peaks with those of Fig. 2-26)

Table 2-4. GC/MS analysis and peak area of the hopanoids.

Core Depth(m) Ts Tm

C29a(5Hopane

C29paHopane Oleanane CSOctP

Hopane Moretane C31SHomohopane

C31RHomohopane

E120.6 - - 768783 73933 - 239096 126949 - 147155E129.3 170656 318809 1780872 273743 - 839035 232252 339768 610590E250.3 - - 2095444 387453 130360 859256 136231 - -

E279.9 - - 404676 88501 59421 391083 71312 87125, 149407E290.9 117449 181752 656104 139101 - 765489 200238 119396 444986

ES E310.3 - - 633047 187605 - 465231 127038 - 113602E370.7 299280 609811 2671996 2516871 764719 4732907 1788750 796276 2717119E429.4 386628 723468 2046574 1503677 1325272 5006554 1467963 859452 2413872E518 454259 886194 3811911 4558306 3717916 7348964 1737664 1877877 3457163E560.3 - 398353 1137603 1738773 1358806 3850058 1133084 911546 1571475E570 282075 701487 1534146 1326158 600199 4373496 1531442 1348795 2463544E610.5 1728121 4999351 14504187 6243450 1739504 26194267 2997446 9877328 12354625F110 359051 766474 3446024 692519 497185 1767200 868528 376421 543913Ft 20 - 204424 1573021 299962 - 768253 236631 120337 439452Ft 60.2 124440 123136 1158115 - 161642 566105 195445 - 'F210 - 306306 2771151 583429 - 1515778 578301 - 987079F280 - 941520 3668513 3648498 1225721 7630781 2383725 1062131 3617917

FS F351 532949 863423 3677507 3832471 1359206 5969995 1558233 651686 2270235F401.5 462389 793523 2182110 - 623196 5646501 1009660 1004140 2343650F489.4 317119 660327 4060087 2503261 - 5970976 1305790 949359 2228640F549.7 171399 334848 996004 1100784 616110 2607861 606249 638611 1416303F599.7 958090 2976028 1235108 2921314 3206105 14517849 2481788 2191523 3217960F667.2 2527355 13634565 6420005 12917711 5063093 45707999 - 11535980 -

Table 2-5. GC/MS analysis and peak area of the steranes.

Core Depth C27ctctaR C28actoR C29actaS 29tipp20R 29OPP20S C29actaR CSOctcttt(m) Sterane Sterane Sterane Sterane Sterane Sterane Sterane

E120.6 687094 1013337 - 53644 319179 870251 100386E129.3 2104021 3565835 - - 1086700 2671435 491475E250.3 2375795 2942178 - - 943736 2350339 330658E279.9 694528 885143 - - 299331 821108 113806E290.9 1174654 1395592 - - 532168 1265061 200101

ES E310.3 975528 744645 - - 427287 1185437 169087E370.7 6686841 9792027 1530887 - - 5397486 6958869 1590307E429.4 7173561 7769841 807165 894717 3743977 9582306 1808068E518 5071002 9972731 1914902 - 5239656 6467271 1088185

E560.3 4942405 6131308 758422 668071 1937094 4869717 883233E570 3397054 3451583 464311 - 1445508 3554259 610965

E610.5 2958255 2413595 471105 - 1969314 4510430 494283F110 4271884 7210373 - . - 2212064 4858820 658219F120 1598598 2506391 - - 900986 2079459 319439

F160.2 806694 1012404 - - 429345 874794 164574F210 2562579 2869810 328199 - 1527030 2969564 595013F280 11216120 14103320 - - 5450500 15314081 1839095

F5 F351 14552176 11217835 846791 - 5032593 15082211 2235664F401.5 5443413 6499830 899696 - 3235387 7156909 1209861F489.4 9374178 17180364 1398503 - 4671151 12935609 1371498F549.7 4064830 4764420 - - 1441311 4011356 669184F599.7 6346105 8965638 1059192 - 2326479 6458435 1226296F667.2 - 616985 255730 - 767331 2871920 344754

-72-

Table 2-6. GC/MS data for maturity and source characterization parameters.

Core Depth Ts/fm Moretane C31S/R Oleanane/C C29/C30 %C27ctctct %C28aaa %C29aaa %20S CSOHopane/(m) /C30Hopane Homohopane 30Hopane Hopane Sterane Sterane Sterane Sterane C29Steranes

E 120.6 - 0.53 - 0 3.22 26.73 39.42 33.85 - 0.19E 129.3 34.87 0.28 0.56 0.00 2.12 25.22 42.75 32.03 - 0.22E250.3 - 0.16 - 0.15 2.44 30.98 38.37 30.65 - 0.26E279.9 - 0.18 0.58 0.15 1.03 28.93 36.87 34.20 - 0.35E290.9 39.25 0.26 0.27 0.00 0.86 30.63 36.39 32.98 - 0.43

E3 E310.3 - 0.27 - 0.00 1.36 33.57 25.63 40.80 - 0.29E370.7 32.92 0.38 0.29 0.16 0.56 28.53 41.78 29.69, 18.03 0.34E429.4 34.83 0.29 0.36 0.26 0.41 29.25 31.68 39.07 7.77 0.33E518 33.89 0.24 0.54 0.51 0.52 23.57 46.36 30.06 22.84 0.54

E560.3 - 0.29 0.58 0.35 0.30 31.00 38.46 30.54 13.48 0.47E570 28.68 0.35 0.55 0.14 0.35 -32.65 33.18 34.17 11.55 0.80

E610.5 25.69 0.11 0.80 0.07 0.55 29.93 24.42 45.64 9.46 3.77F110 31.90 0.49 0.69 0.28 1.95 26.14 44.12 29.73 - 0.25F120 - 0.31 0.27 0.00 2.05 25.85 40.53 33.62 - 0.26

F 160.2 50.26 0.35 - 0.29 2.05 29.95 37.58 32.47 - 0.43F210 - 0.38 - 0.00 1.83 30.50 34.16 35.34 9.95 0.31F280 - 0.31 0.29 0.16 0.48 27.60 34.71 37.69 - 0.37

FS F351 38.17 0.26 0.29 0.23 0.62 35.62 27.46 36.92 5.32 0.28F401.5 36.82 0.18 0.43 0.11 0.39 28.50 34.03 37.47 11.17 0.50F489.4 32.44 0.22 0.43 0.00 0.68 23.74 43.51 32.76 9.76 0.31F549.7 33.86 0.23 0.45 0.24 0.38 31.66 37.10 31.24 - 0.48F599.7 24.35 0.17 0.68 0.22 0.09 29.15 41.18 29.67 14.09 1.47F667.2 ' 15.64 0.00 - 0.11 0.14 0.00 17.68 82.32 8.18 11.74

3:4#4 5# E#4 370.7m, F#4 210m 44 #44 4-4 C29 aD&44 C30 ctPJL^r^l 4 #4 43711 44#4- 4, ###5:4 C29M /C30 3.44 4# IS-4 "§"4 441 4# 444 4 ## 1 443 Cso344 444# 44$4. C295L4/C30 3:44 47} 1S.4 # 54 4444 44 4 4431 437> 44# 44 444 #4#4 ##4 444, 4444, 4# #4 44# 4444. $4 44 444 434 4# #444 C29

3:44 #41 441 444# 5# #44, 4444, #4 44# 444# 5444 S.4# 44# 43414 444# 4433. 4#44.

C27-29 acta R 344# #414 44 44# #4 4 44 #4 4# 43.4 44' #5# #3 44 45#(transitional zone) 4 4 #34 E# 610.5m, F#- 667.2m 4344# #4 454 #4 ### 4444(Fig.2-28). 4 4##4 #4 #4# 44# 44# #4454 #414 5# 4 # #4 44## 5 44 $711- #44 444 444-# 4444.

#4 3.#4#4 #4# 444# #314-44 E, F#4 4344# 4 #44 %44 44 4533 4#44 E, F# 44##4 44 444# #4 ##4#4 #44 4# #1## # # 44. 44 #5# 444#3 444 44444 C30 ctp M4 C29 344#4 4# 34 E#4 610.5m 43.44 3.774 F# 667.2m 4344 11.74# 444 5# 444 Jl# 3# 344#7> #444(E# 0.19-0.8, F# 0.25-1.47) 44 43# 444 4 #44 444 44#3 4#45## 44^4. 45# Czz-29 a aa R 344# 4#44 #4 444-^44 444 44 44 4# 44 $4# #4 443 4 4##4 44# 437} 44 45# 444# 54 #44# 544 (Fig. 2-28).

—74—

Gs?

▲ E core • F core

Lacustrine

C28 C29

Figure 2-28. Ternary diagram showing C27-29 octet R sterane distribution.

-75-

9

Abundance Ion 217.00 (216.70 to 217.70): F667.D

35000 -

30000 -

25000 -

20000 -

15000 -

10000 -

5000 -

100.0096.00 98.00rime--> 78.00 94.0082.00 84.00 88.00 90.00 92.0080.00 86.00

2-29. Distribution of steranes, F core 667.2m.(Note the CgguaaR peak which shows input of terrestrial organic matter)

Figure

2-4-2. 13 345 H44(Table 2- 6)5414 Cso a3 aUt* JIB] ##4 E#4 3# 120.6m 3&43

0.5345 1#5 #4# 33-s. #4 #4 610.5m 3&43# 0.11# 441

Jl F#4 3 #5 FllOm 3&43 0.49, 4444 F 667.2m 3543# a

414 44 4#44 &14. 4#4 145a #4 1341 341 it

4 44-4 #13# 4# 44 #4 14 3 #57]- 4- 3 #44 l#a l

#4 414# 344a 1## #7] #4 #14 41 43=5 1## 14

14". 1#5 1## #7] #4 14 3 #57} #13 # 3# Rock Eval 1

#44 41 Tmax 314 3 57} 4444 4-4- #41# 34- 131# 1

444. 131 Rock Eval 1 #44 5414 alJt 5# 51 31 3

a# 4# 33 134# 5113 #### 1414.

14 3 #5# 7># 13 41 5 4# 34 53# 44445 Csi&a all S, R4 4# 14 511. #44 3#1 #1434 4## E# 4 4# 3-# 1# #1# 4411 370.7m43 #4 610.5m 43 #7>4# #4(0.27-0.80)# 545 F#435 4#7>35 120m 1# #443# #11 4=3"# 543 1#5 4## #3 #4 14 3157} #7} 4# 3# 3114. 544 4 4-4444 43 #3 #44 41# 355 3# 33 444# 433 4# 3# 4444 4 51 Rock Eval !#34 41 Tmax 14 3## 1 #44 43 3# 33 144 41 13 4# 34 #14# 4444(Table 2-7).

544# 14 34 14# 331# 44441 C% acta #41 S 4 C29 aaa 541 R4 4## 54 1 4 131 313# 543# #34 C29 aaa 544 S3 4#4 # 3# 33141 20%-E 13 1=4 13 4 3# 44# 1414.

—77—

Table 2-7. Rock Eval pyrolysis Data of the Pohang E, F cores.

Depth(m) Gas Oil S2 Tmax OPI GPI TPI TOC HI120.6 0.00 0.42 3.32 401 0.00 0.11 0.11 2.75 120129.3 0.00 0.44 6.93 420 0.00 0.06 0.06 2.66 260250.3 0.00 0.27 7.31 420 0.00 0.04 0.04 2.63 277279.9 0.00 0.29 11.30 425 0.00 0.03 0.03 3.05 370290.1 0.00 0.26 11.03 427 0.00 0.02 0.02 2.96 372

E3 310.3 0.01 0.17 3.56 423 0.00 0.05 0.05 1.82 195370.7 0.00 0.15 3.64 427 0.00 0.04 0.04 1.80 202429.4 0.00 0.25 6.48 420 0.00 0.04 0.04 2.32 279518.0 0.00 0.12 4.22 424 0.00 0.03 0.03 1.56 270560.3 0.00 0.21 6.55 423 0.00 0.03 0.03 2.08 314570.0 0.00 0.26 6.88 426 0.00 0.04 0.04 2.56 268610.5 0.00 0.25 7.43 433 0.00 0.03 0.03 1.79 415110.0 0.00 0.24 7.24 421 0.00 0.03 0.03 2.66 272120.0 0.00 0.21 7.32 423 0.00 0.03 0.03 2.55 287160.2 0.00 0.30 6.61 414 0.00 0.04 0.04 2.47 267210.0 0.01 0.20 5.69 421 0.00 0.03 0.03 2.45 232

FS 280.0 0.00 0.16 5.26 426 0.00 0.03 0.03 2.07 254351.0 0.00 0.22 5.83 421 0.00 0.04 0.04 2.23 261401.5 0.00 0.20 4.89 422 0.00 0.04 0.04 2.06 237489.4 0.00 0.18 6.21 423 0.00 0.03 0.03 1.73 358599.7 0.00 1.79 17.54 425 0.00 0.09 0.09 3.71 472667.2 0.00 1.07 29.50 438 0.00 0.04 0.04 3.74 788

—78 —

2-4-3. S-E-

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314444 41## ##M 4# hs4-s:z#41 1112* 1-4 S

4. 144 41# 1 £# 11124 Ml- HIM 54 1112

14 £4###1 44 4444s fit 41-4 s#*l 24###

1-4 444. £4 44 41# 45.4 £4 1112# Ml-, 24*1

44 £4#* 14 144 44 £4 44 4114 44 #41 is. 4

4=4 44 444 #41 44‘M, E14 610.5m, FI 667.2ml £* *

4 44 #4* 4-4M. 44 £4# Ml 1* £1 44 14#4 1

4 4 Ml 14 14:44 4-144 41s ?M4 4M4- 4444 #

7T-4-1 7}14 4.44SS.1 41 44 #441 444 1# 441 44]

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£4# 144 4# 44 #444.

£4 444 4441 44 £4# 144 141* is 44 4414

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444 44 #4# 4 44 44 £414 4# 4-14- 14£444 #

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-83-

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-M444JE &#4- 4 41§# 444 #4, redox potential, 4

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1-1. #4o|# 444#

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4 #4'4 4 Ci - Cs 444"5#4 #4 55 4 #4# 444## 4 4)4# 4# 4 #4 44 44^4.54 4# ### 7>#44 4 4" $1# 443„4 44 4 54 # 444# #44 444. 4 444###4 44 444 44# 11 7>4## 54 4# (diffusion; Rosaire,

1974; Kartsev et al., 1959; Baijal, 1962; Siegel, 1974; Donovan and

Dalziel, 1977; Duchscherer, 1980, 1981, 1983; Dennison, 1983), ##

(effusion! Rosaire, 1940; Kartsev et al., 1959; Baijal, 1962; Donovan

and Dalziel, 1977; Dennison, 1983; Duchscherer, 1981,1983), ## #4

4 4 444"54 4"4 #4 (Pirson, 1960, 1962; Donovan gnd

Dalziel, 1977; Davidson, 1981; Duchscherer, 1981, 1983), 4#

—84—

(permeation; Horvitz, 1950; Baijal, 1962) 43 $14.

34313## (1976)# 44# "#4, €4 ## 3 #3#3 S# 441 33 #55, 4 system^ 5# ##3 #5* #

143 4# 4455 33 444 3 ”55 33414. 3 33 3

344 444 44## 443 344 4444 halo ## 43#

4 4 §14. 54 444 344 43 444 3 44 3433 4#444 #1# 43 4344 34 #3 3 333# 4435, 43= 443 44## 3 3 #13 433 34 37}3#343 3a 34 34 435.44 44 44 C6+ 44### 33 4 4 33 4

4# (effusion)4 44## 33#14 4 4 45.5. 5^3# #

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3 4 3#34# 44## 3 31 #4 34# 3 #43 ##33# 4# #3 #3 313 3 4 #3 #3 3# ## 344 ^#7} 4 4## 44# #443 355 344# 33 713445 #444(Pirson, 1960, 1963, 1964, 1969; Donovan and Dalziel, 1977;

Roberts, 1980; Davidson, 1982, 1984). 34 43 #355 3#4#

—85 —

6## 44#£4 #4 #### 44-4 4"9=% 144 4#4*

1144-3. 4444. Pirson4 Roberts# "44 #4#"9

(forced draft)” ## “444 4# (deep water discharge)” 4 ##

444^4. °1 4 -E- °1H # 144 —5. 44 4 #4 44 4 # 44 44 441 7>44t11 4 £44 43.44 4444 44#£

444 4#444, 444 #4 44## 444 #444 444 £444 44# 444. 344 £44 44, 4# 4 444 444 44 4 4#4#44 44 41 #4 <344 44#, Pirson

(1960, 1963, 1964, 1969), Roberts (1980, 1981)4 4## #7>#4

4£3 3.44. 44 #4 #4# £# 41## ##7} 44#£ ### #444 -45E4 44#£ 4 4^4 (## 44) 444# 1 #4# 4# #7>#4 4£3 1444. 44# (D 444 44# #£. ###4 0# 414# #44# #44 44 #4 3#4 #7} #44# 4# 444£, (2) 44 #4 £#4 444 444 #1 44 4 # #47> &4. 4 4 4# #44 #44 #4 #43:## #7H ##43i 4# 43-4 4# ## 4# 4#4* 11# 44 31, ##4 ## C6+ 44##4 #4# 7>4# 444.

4#44 #4 4 7>?§ 44 44#£4 4 4^4 4# 444#

# 4144# 4# 7}4#4 $%£4, 3 #44£ &44 3.71 (# £4£ 371)4 7>3 4 $4 44 ##4 444 44#£ 44^4

4 4#444# MacElvain (1969)4 444 #44 ##4 $14 3.

44. 4 4 4# 44134 4 3&4SZZ.4-4 £44 4# 44 7>4

44# #1# # $14.4# 4## #4 4-71 4# 4 4 7>4 7ii##4 $144 444

4444 ## #4# #4 ##14 04 #4# 44## #44#

i.

—86 —

7>54 ##44. 44* #4 44 444 4 414 4 4# #

44 4-<3 4 4 4#4 4-5.4 7># # <344* 334 44*54 4 44 ##4 (microfracture system)# *4 447]-^ 7] S-4

*#44. 44# 4& 44*5 44*44 *7fl ^ 4# 4#44 #44 #4 *5* 4# 444 *444 44 4#4 44*5 4 4 44 ***4 4# 44 1441- #44 444:4# 4# 4444 4#44. Price et al. (1984)4 444 3=4# -$-44 *5. 244fat 44 #44# 5.4 4 4 4#* #44, #* 5S #4 5. #4# 4 ri 5.4* *44 44*4 (*# 444 44 44* 4) *4 4 #4 S4* 44 4 4 4. 44 4 #4 *4* 5.445. 5. 4*4 * #7] ## *7] 4 #5-' 4 *# #4 45.5.4 444 * 4* 4 44. 4 44 *4 4 #5.4 4* *5. 4*4# a* 4* 4 24 4# #a 44 44*4 4414*4 444* 44* 41# 44. 2-* 4444 4 4 4# *4 #2:*# 4* 4* #4 4 4 4 **4 4 4 4 #*# 7J4 *5. 44. Park Jones* 444 # 4 44 3E* *7H 5L7l 455 4* 714 *44 7>4*“cryptocrack"5.5. #4 4 4 $1## 4*4314 (Davidson, 1963)

1-2. o|^*44 #A|-

4 ##44 54* 441 *# 44*54 44 45.5. 4 44*45 s* 4* 7>44 4 4#* ### 44 45.4- 4* 4 4 4. 4 *44 *#* # 5444 44-4 4 7># #*4 4*4 #4. 5si4 4#414* 4# #47} 44 #544 ##4.

Kartsev et al. (1959)* 5444 **34* *5 4#* 44^

—87—

# 4 7}z15 *4484= (1) 24, (2) #4 5# *4

£4 a]^254e1 45# 4*4 41#*# sa>, (3) a4 *5

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# 4444 #4 44s. 14*4. *57} #4*8 44 847}

4-71 4-E 444 4#& 7M& 4"5 $14. 7}* *44 41# *

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# ## *44. 4*# 5* 244 #** *54* *5o)i 44

4 *4* 4## §14. Strawinski (1955)# 0.61m# #4485,

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4 &4#, 4# 15-30m #4 4*5 ** 49-71- 414 4# 44*

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1980; Rice and Claypool, 1981; Gautier, 1981; and Mattavelli et al.,

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Sampleinjection

Vaccum

, gas move

■ NaCI solution■ water sample I | extracted gas

valve closed(2) valve opened

1X1 clamp —» gas/liquid flow

Fig. 3-1. Scheme of experimental gas extraction device

-104-

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(Fig. 3-14 E).

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—106 —

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organisms) #4 #5 44 4 44 #4]44 4#44 4 4 445 4

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—107 —

5.44(Um)5 4# 44 543 4## <%7\] #3#}2.3. #43 g

4 #44 4 #4 # $13 2680m3 443 4(Telegina

et al, 1963), 3.7] 7} #4 44 X44 3 W4 44^4 4 7)1 4#4

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redox potential°1 444-2., 5. 4(acid)4 A34 55 $14 4#4 -§-

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chips), a 44 & (core sample), 5 5)5 44## 4 4 4 S-# 4 #4

4 4 # 4 # $1 5(Kartsev et al., 1959) 5 4 & # 44 4# 345.

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1(£# plating #1)# 45 agar 44 4 £## ^4 444# 4 54 4 4(colony) ## #444 #4455 5445 #44# # 1 4 4. Michaelis and Riese (1991)# 4 444 4 #1# 544 4 45.# 4155 44#44 4#44# 45484. 4 45# #544 44#57> #7})# 4#, 4#4 4#4# 45# 4#4 4, zif 4 #44 4444. 444 44457} 4 44#4# 44

—109 —

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1987), £ **1*4* ##33 711** Probe* 4*44 4*14

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Diels and Mergeay, 1990) *33, 4# ##14 4*14 4*#4

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3-1-1. 4sxH#|

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## 44 ^ #4 #44 4^4 444*\ 44 444 (Fig. 3-1).

45.44# 44 4 4433 4444 444# 4 4 44 44*44

4 4^44} 444 3# #44 4#4 44#435. 444 44 4 4 44 ### 44 444435 44444. 3# 43# 4 X4 4##4 4 4# 4## 7}## 44 44 44 4X44 34=4 4 #44# 54 50-60cm(Davis, 1967)4Ai 4444 ^#3##

4 #^#3 ##44 ##4 A>#4^4.

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al, 1993) ##a> 4# ### 4#33 # 397fl 444 (A 4 4), 4 4, A>#, 444 3 443 4# #4#4, 3# ##34 #4 #

4# 4435. # 15711 4 4 (B 4 4)4 30711 4 4 (D 4 4)44

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20-30m #433 44444.

—ill—

129°20' •

• P.4,

Y -15

SCALE

Fig. 3-2. Sampling site and location map

3-1-2. x-lE|

414 £4=4 £## 44 £4= lgi- I00ml4 1544 4^4=

4 ^444 244 ^4 44 44=(160rpm)& # # 3044 444

4 4-S-414. 30# 44f ^71)4 4144 4#4 10ml 4# 4

7] ##44 44# 4£# (control)£, 444 44# 44 #£4

#54 (nickel, 600 ppm; cobalt, 500 ppm; cadmium, 100 ppm;

mercury, 20 ppm; zinc, 400 ppm; lead, 500 ppm) Sr 4444.

(pentane, 20 %; hexane, 20 %)# l7>44, 444 4 4 4 4 4 2

44:, 444^ 44 4 44 1444 4&#4 44 4^ 44=# 4 44. 4 4= 4 42? 444 4s.4 44 1044 441££ 44 4444 44 44 4 4 (nutrient agar) 4 4 4 44 4#4 4 30 "C 44 544 4 4=44 144# 44 (colony) 4# 44Jt 4# 4# 41444(CFU; Colony Forming Unit).

3-2. 214114

3-2-1. 4544 4444: 4E 94

4444 4 4 444^4 £ 444^4 444 4541-4 4 444 $14 €4 4:444 44444 444#4# 444 £4=4 4 4# #4#4 44 4 44 44 #4 &tj-. zl^M- 4.# 4544 444^4 ^@#4 #!##£ 4-§-4££, £44 AS

14# 4# #44 #4# £4# 44# 414:%, 444#44 1## 4 $14. £°<M1#, 44 £4415# 444^4 #5 44 44 4 ##14 44 3 44(Chen et al, 1985; Atlas, 1981) 1## 7}#4 4 4# 114 4 44 #4, 441-44 44 #54

113-

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4 €4 €211- €-§-44 4€#4€ €4€44 411 #^-44

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all €€44 4€(Ni), a#5(Co), 45.#(Cd), #l(Hg), 4-4

(Zn), 4(Pb)€ 44 #1 ##44, 4 4 (pentane), 4 4 (hexane) 1

##€ 44##1 44 4 4 #5#5 ##44 244, 44### 1

44 42144 444 4 s#4#€ €#11 #44^4 (Tables

3-1 to 3-8). a €4 ##44 Nil 600ppm55 4 4 €4 61.5%,

500ppm Co# 64.9%, lOOppm Cd# 85%, 20ppm Hg# 20%, 400ppm

Zn# 64.6%, 500ppm Pb# 63.7%€ €####4 44535, 444#

4 €44 €44 44 20%5 €414 €4 60.2%4 61.6%€ €#

€#11 4444, 4 #5.1- €€#55. €€444.

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4 €4 €#14 €4 #4 4 #55. €€445, a Hg# 4155 a.€ 544 €55 444# €€€ 4€ 24 45# €€€-5# €# 2.5%€ €#11 54 #5# 444 141 €4 Cd4# 4 € #47} 444, 4 #55 €€4534. 41 ##44 44##€ #5# Michaelis and Riese(1991)€ 54451 4 €4 #54 45 1 4 Znl €445# 45€ #1 #5535, a 444 4411 4214 €€ #54 45# €4# €1 #4 444 €44 44 441€1 144 44##4 ##14 #57} #€4 4 4 "tl 4 €1 1 4^4.

-114-

Table 3-1. Survival rate(%) for nickel(600 ppm) in Taejon area.

Site Total No. Survival No. Survival rate ( % )1 1.5 x 10* 6.6 x 107 44.02 5.3 x 106 4.5 x 106 84.93 1.8 x 107 7.0 x 106 38.94 4.1 x 107 3.2 x 107 78.1

Average 61.5

Table 3-2. Survival rate(%) for cobalt(500 ppm) in Taejon area.

Site Total No. Survival No. Survival rate ( % )1 1.5 x 10* 1.1 x 10* 73.32 5.3 x 10* 2.5 x 10* 47.23 1.8 x 107 1.1 x 107 61.14 4.1 x 107 3.2 x 107 78.1

Average 64.9

Table 3-3. Survival rate(%) for cadmium(100 ppm) in Taejon area.

Site Total No. Survival No. Survival rate ( % )1 1.5 x 10* 1.4 x 10* 93.32 5.3 x 10* 4.2 x 10* 79.33 1.8 x 107 1.3 x 107 72.24 4.1 x 107 3.9 x 107 95.1

Average - 85.0

Table 3-4. Survival rate(%) for mercury(20 ppm) in Taejon area.

Site Total No. Survival No. Survival rate ( % )1 1.5 x 10* 2.0 x 10* 1.32 5.3 x 10* 3.8 x 10* 71.73 1.8 x 107 5.0 x 10* 2.84 4.1 x 107 1.7 x 10* 4.2

Average 20.0

-115-

Table 3-5. Survival rate(%) for zinc(400 ppm) in Taejon area.

Site Total No. survival No. Survival rate ( % )1 1.5 x 10* 9.7 x 107 64.72 5.3 x 106 3.4 x 106 64.23 1.8 x 107 8.8 x 10s 48.94 4.1 x 107 3.3 x 107 80.5

Average 64.6

Table 3-6. Survival rate(%) for lead(500 ppm) in Taejon area.

Site Total No. Survival No. Survival rate ( % )1 1.5 x 10* 8.4 x 10Y 56.02 5.3 x 106 2.6 x 106 49.13 1.8 x 107 1.2 x 107 66.74 4.1 x 107 3.4 x 107 82.9

Average 63.7

Table 3-7. Survival rate(%) for pentane(20 %)in Taejon area.

Site Total No. Survival No. Survival rate ( % )1 1.7 x 10* 1.2 x 10* 70.62 8.5 x 106 3.6 x 106 42.4 •3 1.8 x 107 1.1 x 107 61.14 3.3 x 107 2.2 x 107 66.7

Average 60.2

Table 3-8. Survival rate(%) for hexane(20 %) in Taejon area.

Site Total No. Survival No. Survival rate ( % )1 8.5 x 10Y 4.9 x 10Y 57.72 6.1 x 10s 2.8 x 106 45.93 1.4 x 107 1.1 x 107 78.64 2.8 x 107 1.8 x 107 64.3

Average 61.6

-116-

3-2-2. 94444 gy.

944444 44# s# 4 s# #4 s#44 ###3 ##

# -f- ###4 ###33 4 4##• 44 #4 control ^93 34-

# 9#4 4 5-44 3g# ##4 4=71)$^ 4# Table 3-94 44-1&

4.A, B, C.D^I 4 4 #3 4 Hi; #3 4 %>-A3 94€

A 4 44 45 1.15 x 1074 °1#93 C 4 44 9-99 x 106

4.441 4 4, 44, 444 h44-31 B 444 1.12 x 108,

3#jl d 4 44 2.18 x 10893 #444454 4h4 ^4 1-9

43i 444- 3.44 444 =14 #94 3 #9 444 4 £°<M

4444 ##4 4 454 494 4###31(Pelczar, 1986), 4#

94444 44 ##444)54 494 44444 444 4 4 4 5#4# 5.14 x 1074-9 e #4* 944 ##4. 444 444 4 44 4= 5cm 494 44 44 444 4s.4- 94444 45 50cm

44 44 44 4 4s s#4 4#4 944 4## ## e#(rhizosphere)4 #41 $14 4#d14- 4-S44. 444 444 4 4# 44 4##4-M 71) #4 4^14 4 4 414# 4 #94 4 4## # 4 ## 4# 5# 44 44 4s*fl47> 49444 # 4S3 44 €4.

3-2-3. A 4 4 3 #4 5 4 #### 4##9ol| mil ^##

94 3## 4#4 #4 #4# 44 100-150m 4493 # 39

4444 S44S-E 44# A 4 4# #s 4 493 944 4# # 4 4 (Chough e! a/., 1993)44. 4 A4 4 s## ###4 44#

93 4 444 4### 94# #4(Tables 3-10 to 3-17), 444

-'■ V

-117-

Table 3-9. Average number of heterotrophic bacteria in site A, B, C and D

Site (A)Average No

ofHeterotroph

1 1.92 X 102 7.13 X 10®3 1.31 X 10%4 1.53 X 10%5 4.80 X 10%6 1.91 X 10%7 5.23 X 10®8 5.93 X 10®9 1.23 X 10%10 1.08 X 10%11 5.59 X 10®12 7.58 X 10®13 8.11 X 10®14 7.91 X 10®15 1.08 X 10%16 1.49 X 10%17 1.18 X 10%18 1.12 X 10%

. 19 9.81 X 10®20 7.13 X 10®21 7.46 X 10®22 1.49 X 10%23 1.15 X 10%2'4 2.18 X 10®25 1.25 X 10%26 3.68 X 10®27 4.54 X 10®28 2.40 X 10®29- 3.53 X 10®30 1.23 X 10%31 3.27 X 10%32 1.65 X 10%33 1.53 X 10%34 3.41 X 10®35 1.24 X 10%36 1.01 X 10%37 3.23 X 10®38 2.26 X 10%39 ‘ 8.15 X 10®

Average 1.15 X 107

Site (B)Average No.

ofHeterotroph

Site (D)

1 1.54 x 10% 12 2.80 x 10® 23 8.51 x 10% 34 1.48 x 10® 45 4.20 x 10% 56 5.14 x 10% 6

■ 7 2.65 x 10% 78 8.01 x 10% 89 1.35 x 10® 910 2.02 x 10® 1011 2.47 x 10% 1112 2.07 x 10% 1213 1.99 x 10® 1314 3.50 x 10® 1415 5.05 x 10% 1516 8.14 x 107 16

1718192021222324

Average 1.12 x 10*

Site (C)Average No.

of 25Heterotroph 26

1 2.45 x 10% 272 1.18 x 10% 283 1.74 x 10% 294 8.30 x 10® 30

Average 9.99 x 10®Taejeon Average No.

ofHeterotrophArea

1 1.44 x 10®2 5.80 x 10%3 1.75 x 10%4 3.84 x 107

Average 5.14 x 107 Average

Average No. of

Heterotroph1.74 X lo:4.47 X 10,5.24 X io!1.67 X 10,1.20 X 101.55 X 104.06 X 103.68 X 105.69 X 10,6.71 X 10,4.36 X 10:3.46 X 10,4.94 X 103.43 X 103.59 X 103.09 X 103.41 X 102.43 X 103.04 X 10:4.51 X 10,2.41 X 101.47 X 104.91 X 101.73 X 10:9.58 X 10,1.67 X 104.61 X 108.25 X io:9.11 X 10,1.53 X 10*

2.18 x 10s

—118—

Table 3-10. Survival rate for nickel(600 ppm) in site A

Site TotalNo.

SurvNo.

Survivalrate(%) Site Total No. Survival

No.Survivalrate(%)

1 1.5 X 10 8.0 X 10® 53.3 21 8.3 X Tof 6.9 X 10® 83.12 3.7 X io! 6.7 X 10® >100.0 22 8.6 X 10® 5.0 X 10® 58.13 1.0 X 10? 1.3 X 10? >100.0 23 1.3 X 10" 1.2 X 10" 92.34 2.4 X io? 2.8 X 10 >100.0 24 1.8 X 10® 1.9 X 10® >100.05 2.2 X 10? 6.0 X 10® 27.3 25 8.5 X 10® 6.4 X 10® 75.36 1.1 X 10® 8.0 X 10® 72.7 26 8.0 X 10® 1.7 X 10® >100.07 3.3 X 10® 6.2 X 10® >100.0 27 4.5 X 10® 4.2 X 10® 93.38 5.2 X 10® ND „ ND 28 3.0 X 10® 2.5 X 10® 83.39 2.2 X 10® 3.8 X 10° >100.0 29 4.0 X 10® 3.0 X 10® 75.010 1.3 X 10® 1.1 X 10® 84.6 30 1.1 X 10" 9.5 X 10® 86.411 4.5 X 10® 4.4 X 10® 97.8 31 9.5 X 10® 7.7 X 10® 8.112 1.4 X 10? 7.0 X 10® 50.0 32 2.0 X 10" 1.1 X 10" 55.013 1.0 X 10" 8.7 X 10® 87.0 33 1.2 X 10" 7.0 X 10® 58.314 8.2 X 10® 8.0 X 10® 97.6 34 5.3 X 10® 5.0 X 10® 94.315 1.2 X 10? 4.3 X 10® 35.8 35 1.1 X 10" 1.0 X 10" 90.916 1.2 X 10" 5.7 X 10® 47.5 36 1.7 X 10? 6.0 X 10® 35.317 9.0 X 10® 3.0 X 10® 33.3 37 4.0 X 10® 1.0 X 10® 25.018 1.5 X 10? 1.3 X 10" 86.7 38 3.3 X 10" 3.1 X 10" 93.919 1.2 X 10? 8.2 X 10® 68.3 39 1.0 X 10" 7.6 X 10® 76.020 9.9 X 10® 7.5 X 10® 75.8

Average 73.7ND : not detected

Table 3-11. Survival rate for cobalt(500 ppm) in site ASite Total

No.Survival

No.Survivalrate(%) Site Total No. Survival

No.Survivalrate(%)

1 6.5 X 10' 8.0 X 10® 12.3 21 2.0 X 10° 1.4 X 10° 70.02 3.3 X 10 2.4 X 10® 72.7 22 6.2 X 10? 6.3 X 10? >100.03 1.3 X 10" 1.3 X 10" 100.0 23 1.3 X 10" 1.2 X 10" 92.34 1.7 X 10" 1.6 X 10? 94.1 24 2.0 X 10® 1.0 X 10® 50.05 4.8 X 10? 7.0 X 10? >100.0 25 1.3 X 10® 5.0 X 10® 38.56 2.6 X 10" 1.7 X 10" 65.4 26 3.3 X 10® ND . ND7 4.1 X 10® 4.0 X 10® 97.6 27 8.0 X 10® 6.0 X 10® 75.08 9.0 X 10® 7.0 X 10? >100.0 28 1.1 X 10® 3.0 X 10" 27.39 1.4 X 10" 6.0 X 10® 42.9 29 2.0 X 10" 2.0 X 10" 100.010 5.0 X 10® 1.7 X 10" >100.0 30 2.8 X 10? 2.6 X 10" 92.911 2.9 X 10® 2.5 X 10® 86.2 31 1.1 X 1.0? 1.2 X 10" 100.012 2.3 X 10® 2.7 X 10® >100.0 32 1.2 X 10" 1.1 X 10" 91.713 6.2 X 10® 6.2 X 10 100.0 33 8.5 X 10® 5.5 X 10® 64.714 9.8 X 10® 8.8 X 10® 89.8 34 8.0 X 10" 9.0 X 10" >100.015 4.9 X 10® 5.0 X 10® >100.0 35 7.0 X 10® 6.8 X 10 97.116 4.7 X 10® 5.0 X 10® >100.0 36 5.3 X 10® 5.5 X 10® >100.017 5.4 X 10® 4.8 X 10® 88.9 37 6.0 X 10" 2.9 X 10® >100.018 9.5 X 10® 8.6 X 10® 90.5 38 2.1 X 10® 1.8 X 10® 85.719 6.3 X 10® 3.9 X 10® 61.9 39 2.2 X 10® 1.4 X 10® 63.620 2.8 X 10® 2.5 X 10® 89.3


—119 —

Table 3-12. Survival rate for cadmium(100 ppm) in site A

Site TotalNo.

SurvivalNo.

Survivalrate(%) Site Total

No.Survival

No.Survivalrate(%)

1 8.7 X 10° 5.2 X 10% 6.0 21 7.7 X 10® 5.1 X 10® 66.22 3.7 X 10® 1.7 X 10® 45.9 22 6.8 X 10® 3.8 X 10® 55.93 7.8 X 10® 8.3 X 10® >100.0 23 1.4 X 10% 1.1 X 10% 78.64 9.8 X 10® 1.2 X 10% >100.0 24 2.8 X 10® 1.5 X 10® 5.45 6.3 X io% 1.9 X 10% 30.2 25 7.5 X 10 2.8 X 10® 37.36 8.9- X 10® 6.8 X 10® 76.4 26 2.0 X 10® 1.0 X 10® 50.07 5.5 X 10® 4.5 X 10® 81.8 27 7.1 X 10® 6.6 X 10® 93.08 4.1 X. 10® 3.5 X 10 85.4 28 1.2 X 10 1.4 X 10® >100.09 1.3 X 10% 1.7 X 107 >100.0 29 7.0 X 10® 1.4 X 10® >100.010 1.3- X 10% ND „ ND 30 1.3 X 10% 1.1 X 10% 84.611 4.2- X 10® 1.5 X 10: 35.7 31 2.2 X 10% 2.5 X 10% >100.012 1.6 X 10% 1.6 X 10% 100.0 32 3.2 X 10% 2.6 X 10% 81.313 8.0’ X 10® 8.0 X 10® 100.0 33 1.1 X 10% 8.0 X 10® 72.714 9.6 X 10® 6.5 X 10® 67.7 34 2.4 X 10® 1.3 X 10® 54.215 1.1 X 10% 1.0 X 10% 90.9 35 2.5 X 10% 2.2 X 10% 88.016 4.9 X 10® 3.3 X 10® 67.3 36 9.1 X 10® 7.9 X 10® 86.817 5.5i X. 10® 5.7 X 10® >100.0 37 1.0 X 10® 1.0 X 10® 100.018 4.5 X 10® 5.0 X 10® >100.0 38 2.6 X 10% 3.1 X 10% >100.019 9.5 X 10® 8.3 X 10® 87.4 39 9.5 X 10® 1.0 X 107 >100.020 5.1 X 10® 4.6 X 10® 90.2

Average 76.8D : not detected

Table 3-13. Survival rate for mercury(20 ppm) in site A

Site Total' No. SurvivalNo.


No.Survivalrate(%)

1 5.3- X To!" 5.0 X 10® 9.4 21 1.1 X 10% 7.0 X 10® 6.42 7.0 X 10 4.0 X 10® 57.1 22 8.5 X 10® 1.0 X 10® 11.83 1.7 X 10% 1.2 X 10® 7.1 23 1.2 X 10% 1.1 X 10® 9.24 2.2 X 10% 1.4 X 10 6.4 24 2.1 X 10% ND „ ND5 1.9- X. 10% 5.0 X 10® 2.6 25 1.3 X 10% 5.0 X 10: 3.86 7.0 X 10® 2.0 X 10 2.9 26 5.4 X 10® 1.0 X 10® 1.97 5.1 X. 10% 1.0 X 10 2.0 27 3.6 X 10® ND ND8 3.5 X 10 5.0 X 10® 14.3 28 3.9 X 10® ND ND9 1.6 X 10% 2.6 X 10® 16.3 29 3.5 X 10® ND ND10 1.1 XL 10% 1.3 X 10 11.8 30 9.4 X 10® ND „ ND11 3.8 X 10® 2.0 X 10® 5.3 31 4.1 X 10% 9.0 X io: 2.212 2.7 X 10® 8.0 X 10 29.6 32 1.7 X 10% 9.0 X 10® 5.313 5.4 X 10® 1.0 X 10 1.9 33 7.9 X 10® 1.3 X 10 16.314 9.7 X 10® 3.0 X 10® 3.1 34 6.5 X 10® 2.0 X 10® 3.115 1.2 X 10% 3.0 X 10® 2.5 35 1.5 X 10% 3.1 X 10® 20.716 1.5 X 10% ND „ ND 36 9.6 X 10® 1.5 X 10 15.617 2.7 X 10% 1.0 X io: 0.4 37 5.7 X 10® 1.0 X 10® 1.818 1.4 X 10% 2.0 X 10® 14.3 38 2.8 X 10% 1.9 X 10® 6.819 8.0 X 10® ND _ ND 39 1.2 X 10® 1.0 X 10® 8.320 1.4 X 107 oC

O X 10° 2.1Average 9.5

ND : not detected

—120—

Table 3-14. Survival rate for zinc(400 ppm) in site A

Site Total No. SurvivalNo.


No.Survivalrate(%)

1 1.0 x 10: 6.0 X 10® 60.0 21 1.0 X 10' 7.8 X 10® 78.02 5.4 X 10® 5.0 X 10® 92.6 22 1.1 X io! 7.7 X 10® 70.03 2.2 X ioI 2.2 X iol ,100.0 23 1.3 X io! 8.5 X 10® 65.44 1.5 X 10l 1.1 X iol 73.3 24 3.5 X 10® 2.8 X 10® 80.05 4.0 X io! 2.9 X io7 72.5 25 1.8 X iol 1.1 X 107 61.16 2.2 X 101 2.3 X 106 >100.0 26 1.3 X 107 2.0 X 10® 15.47 4.8 X 10® 6.7 X 10® >100.0 27 9.0 X 10® 7.0 X 10® 77.88 8.8 X 10® 7.3 X 10® 83.0 28 3.9 X 10® 3.5 X 10® 89.79 1.5 X iol 1.6 X 10® >100.0 29 6.7 X 10® 6.6 X 10® 98.510 ' 1.3 X io! 4.0 X 10® 30.8 30 8.4 X 10® 4.7 X 10® 56.011 6.0 X 10® 9.0 X 10® >100.0 31 2.7 X 10 2.3 X 10 85.212 1.3 X io! 1.9 X 107 >100.0 32 2.3 X io7 1.8 X 107 78.313 7.0 X 10 5.9 X 10 84.3 33 1.1 X 107 3.0 X 10® 27.314 1.0 X io7 1.0 X 10 100.0 34 5.2 X 10® 3.7 X 10® 71.215 1.4 X 107 1.3 X IO! 92.9 35 9.5 X 10® 7.6 X 10® 80.016 1.3 X 106 1.2 X iol 92.3 36 8.8 X 10® 9.8 X 10® >100.017 8.4 X 10® 1.0 X 107 >100.0 37 1.8 X 10® 1.7 X 10® 94.418 1.5 X io! 1.3 X io! 86.7 38 2.6 X 107 2.0 X 107 76.919 9.8 X 10® 3.2 X 10® 32.7 39 1.5 X 107 9.0 X 10® 60.020 6.0 X 10® 4.0 X 10® 66.7

Average 77.8

Table 3-15. Survival rate for lead(500 ppm) in site A



No.Survivalrate(%)

1 2.2 X "To!" 1.3 X 10' 59.1 21 9.9 X 10® 5.6 X 10® 56.62 7.9 X 10® 5.2 X 10® 65.8 22 1.1 X 107 5.8 X 10® 52.73 1.2 X 107 8.7 X 10® 72.5 23 8.6 X 10® 6.7 X 10® 77.94 9.7 X 10® 8.2 X 10® 84.5 24 3.1 X 10® 2.3 X 10® 74.25 4.7 X 107 3.5 X io! 74.5 25 3.3 X 107 2.4 X io7 72.76 3.6 X io! 1.8 X io! 50.0 26 4.7 X 10® 2.3 X 10® 48.97 5.7 X 10® 5.6 X 10® 98.2 27 4.5 X 10® 6.0 X 10® >1008 7.8 X 10® 5.1 X 10® 65.4 28 7.0 X 10® 6.0 X 10® .09 7.5 X 10® 5.5 X 10® 73.3 29 9.1 X 10® 7.3 X 10® 85.710 1.8 X 107 1.1 X io7 61.1 30 1.3 X 107 6.4 X 10® 80.211 1.2 X 107 1.1 X io! 91.7 31 6.0 X io! 4.9 X io! 49.212 5.6 X 10® 4.8 X 10® 85.7 32 1.5 X io7 1.7 X io7 81.713 1.2 X io! 8.9 X 10® 74.2 33 6.6 X 10® 5.7 X 10® >10014 1.1 X 107 1.0 X io7 90.9 34 2.1 X 10® 1.5 X 10® .015 1.7 X io! 1.5 X 107 88.2 35 1.5 X io! 1.3 X io7 86.416 5.5 X 107 2.7 X 107 49.1 36 1.6 X io7 1.2 X io7 71.417 1.8 X io7 1.6 X io! 88.9 37 4.8 X 10® 3.4 X 10® 86.718 1.9 X io! 1.6 X io! 84.2 38 3.6 X io! 1.9 X io7 75.019 1.2 X 10 7.6 X 10® 63.3 39 8.0 X 10® 5.2 X 10® 70.820 9.1 X 10® 5.5 X 10® 60.4 52.8

65.0Average 73.6

121-

Table 3-16. Survival rate for pentane(20 %) in site A



No.Survivalrate(%)

1 1.1 X 10% 6.0 X 10! 5.5 21 5.8 X "ToT 3.0 X 10! 51.72 1.4 X 10% 1.6 X 10® 11.4 22 1.0 X 10% 1.9 X 10® 19.03 1.2 X 10% 3.6 X 10® 30.0 23 9.8 X 10® 1.7 X 10 17.34 1.1 X 10% 4.5 X 10® 40.9 24 2.7 X 10® 8.0 X 10® 29.65 6.9 X 10% 2.3 X 10® 3.3 25 1.4 X 10% 1.8 X 10 12.96 3.9 X 10% 3.0 X 10® 7.7 26 1.9 X 10® 2.0 X 10% 10.57 7.3 X 10® 1.4 X 10® 19.2 27 3.6 X 10® 7.0 X 10% 19.48 6.2 X 10® 1.8 X 10® 29.0 28 2.0 X 10® 6.0 X 10® 30.09 1.8 X 10% 1.6 X 10® 8.9 29 3.2 X 10® ND „ ND10 2.4 X. 10% 2.0 X 10® 8.3 30 8.7 X 10® 1.1 X 10! 12.611 3.6 X 10® 6.0 X 10® 16.7 31 7.5 X 10% 2.8 X 10% 3.712 3.4 X' 10® 9.0 X 10 26.5 32 1.8 X 10® 3.3 X 10% >100.013 7.0 X 10® 5.0 X 10® 7.1 33 1.6 X 10% 2.4 X 10% 15.014 7.1 X 10® 2.1 X 10® 29.6 34 3.0 X 10® 1.6 X 10% 53.315 8.2' X 10® 1.4 X 10® 17.1 35 1.7 X 10% 9.7 X 10% 57.116 5.0 X 10® 7.0 X 10% >100.0 36 1.2 X 10% 5.9 X 10% 49.217 7.8- X- 10® 7.0 X 10® 9.0 37 7.7 X 10® 1.4 X 10% 18.218 1.4 X 10® 5.0 X 10® 35.7 38 2.3 X 10% 7.9 X 10% 34.319 1.7 X. 10% 6.0 X 10® 3.5 39 7.3 X 10® 3.2 X 10® 43.820 4.6 X 10® 1.2 X 10® 26.1

Average 26.7SID : not detected

Table 3-17. Survival rate for hexane(20 %) in site A

Site TotalNo.

SurvivalNo.

Survivalrate(%) Site Total

No.Survival

No.Survivalrate(%)

1 ITT x 10% 2.3 x 10% 13.5 21 5.0 x 10% 2.6 x 10% 52.02 1.2 x 10% 7.0 x 10% 5.8 22 1.6 x 10% 1.8 x 10% >100.03 V.V x 10% 8.4 x 10% 76.4 23 8.3 x 10% 3.0 x 10% 36.14 1.4 x 10% 5.2 x 10% 37.1 24 1.2 x 10% 7.0 x 10% >100.05 7:6- x: 10% 3.3 x 10% 4.3 25 5.0 x 10% 3.5 x 10% 70.06 1.3 x 10% 3.0 x 10% 23.1 26 1.0 x 10% 1.0 x 10% 100.07 6.0; x 10% 5.6 x 10% 93.3 27 3.2 x 10% 1.8 x 10% 56.38 6:0 x 10® 3.0 x 10% 5.0 28 1.6 x 10% 1.0 x 10% 62.39 . 1.3- x 10% 4.9 x 10% 37.7 29 1.0 x 10% 3.0 x 10% 30.010 1.2' x, 10% 3.1 x 10% 25.8 30 6.9 x 10® 2.3 x 10% 33.311 7.7 x 10% 1.1 x 10% 14.3 31 1.6 x 10% 2.3 x 10% 14.412 3.6 xx 10% 7.0 x 10% 19.4 32 1.1 x io! 4.8 x 10% 43.613 9.3 x 10% 2.0 x 10% 21.5 33 5.7 x 10% 3.3 x 10% 57.914 6.7 x. 10% 2.5 x 10% 37.3 34 2.7 x 10% 9.0 x 10% 33.315 7.3 x 10% 2.9 x 10% 39.7 35 5.9 x 10% 3.7 x 10% 62.716 9.6 x 10®. 6.4 x 10% 66.7 36 8.1 x 10% 5.9 x 10%, 72.817 1.3 x- 10% 1.1 x 10% 8.5 37 1.0 x 10% 6.0 x 10% 60.018 1.1 x: 10% 1.2 x 10% 10.9 38 6.3 x 10® 4.1 x 10% 65.11920

3.9 x. 10% 5.5 x. 10®

2.0 x 10%5.0 x 10®

. 5.19.1

39 1.2 x 107 6.4 x 10® 53.3

Average 42.5

-122-

44 6 9*4 9*4 #4934] tflsfl 2.5 4595#* 44444, £5 5#* 44 94 4 35. #44 4 4 4"445 4 4* #4 # 9 &$4. A 444 9994 NH 44 99 95*5 73.7%, Co5 82.6%, Cd5 76.8%, Hg5 9.5%, Zn* 77.8%, Pb5 73.6%, 3.431 #4934 ##5 26.7%, 444 42.5%4 95 #4 4444 444 4 £44 44 9995 99 10%4£ 55 95** 4445 44 #493 9**5 14 45 95** £4 4 4 4 4 444 4 £45 4^4 999* Sr4 #443 44 4 £5. 4444. 3a14 Mt^°1 4# .95*5 44444 441

44 45 435. 44#54, 4 44 44 *44*3 944 5# 93* *& 4*35. * 4 444'444 4 4 44] 4**54 # 4 9#* 4# 4 4 4 3, £ Z*4 #493*4 3#4 4 #445-4 #1# 44-4-3 9##4.

3-2-4. B 44 £44154 #594 #493011 41# *5#£#55s4 9-4 44 445- #435. 4435 54* 44

44# B 4 45 95. 4#, 4#, 4#4 34#3 4* #1# 4 4 4 4(Chough, 1993). 4 B '4 43 44 100-150m #435. *4 * 44- 5 16 4 4 44 £5* 4444 5954 #4934 441 49#* 34# 44(Tables 3-18 to 3-25), A 444 #4 54* 44 44# 9 44 4444 ##4.

B 4 44 9994 4# 99 95*5 Ni5 67.9%, Co5 82.5%, Cd5 86.0%, Hg5 5.8%, Zn5 82.5%, Pb5 91.7% 4^3, #4934 #45 11.8%, 9*5 8.1%4 95** £4 A4 4 9 49 Ni4 Hg* 94 #£5 9994 4*95*4 55 435 44*3# ##934

-123-

Table 3-18. Survival rate for nickel(600 ppm) in site B

Site Total No. Survival No. Survival rate(%)1 1.4 x 10' 1.4 x 10’ 100.02 2.9 x 10! 2.4 x 10® 82.83 9.0 x 10® 3.7 x 10® 41.14 2.2 x 10® 1.5 x 10® 68.25 2.7 x 10’ 1.2 x 10’ 44.46 3.3 x 10’ 2.1 x 10’ 63.67 3.9 x 10’ 1.9 x 10’ 48.78 3.2 x 10’ 1.5 x 10’ 46.99. 1.5 x 10® 1.1 x 10® 73.310 . 2.0 x 10® 1.7 x 10® 85.011 2.0 x 10’ 1.2 x 10’ 60.012 4.9 x 10’ 3.2 x 10’ 65.313 1.7 x 10® • 1.3 x 10® 76.514 • 3.8 x 10® 3.1 x 10® 81.615 ' 3.3 x 10’ 2.2 x 10’ 66.716 8.2 x 10’ 6.8 x 10’ 82.9

Average 67.9

Table 3-19. Survival rate for cobalt(500 ppm) in site B

Site Total No. Survival No. Survival rate(%)1 2.0 x 10® 2.0 x 10® 100.02 3.5 x 10® 3.4 x 10® 97.13 2.8 x 10’ 2.8 x 10’ 100.04 8.1 x 10® 8.2 x 10® >100.05 1.4 x 10’ 1.4 x 10’ 100.06 9.3 x 10® 6.5 x 10® 69.97 6.0 x 10® 5.0 x 10® 83.38 2.8 x 10’ 1.5 x 10’ 53.69 1.4 x 10® 7.0 x 10’ 50.010 ■ 1.2 x 10® 1.3 x 10® >100.011 9.7 x 10® 7.8 x 10® 80.412 6.2 x 10® 6.6 x 10® >100.0 •13 5.2 x 10’ 3.3 x 10’ 63.5

■ 14 1.4 x 10’ ■ 1.2 x 10’ 85.715 3.4 x 10® 1.8 x 10® 52.916 2.4 x 10® 2.0 x 10® 83.3

Average 82.5

-124-

Table 3-20. Survival rate for cadmium(100 ppm) in site B

Site Total No. Survival No. Survival rate(%)1 2.0 x 10“ 2.7 x 10® >100.02 ' 7.8 x 10’ 2.7 x 10’ 34.63 1.2 x 10’ 1.2 x 10’ 100.04 4.5 x 10® 6.7 x 10® >100.05 1.2 x 10’ 1.4 x 10’ >100.06 1.5 x 10’ 1.2 x 10’ 80.07 2.6 x 10® 2.2 x 10® 84.68 1.7 x 10’ 1.6 x 10’ . 94.19 3.1 x 10’ 2.7 x 10’ 87.1

10 1.0 x 10® 4.2 x 10’ 42.011 1.9 x 10’ 1.6 x 10’ 84.212 3.2 x 10’ 3.6 x 10’ >100.013 2.0 X 10® 2.8 x 10® >100.014 5.9 x 10’ 1.6 x 10® >100.015 1.9 x 10’ 1.6 x 10’ 84.216 6.4 x 10’ 5.5 x 10’ 85.9

Average 86.0

Table 3-21. Survival rate for mercury(20 ppm) in site B

Site Total No. Survival No. Survival rate(%)1 5.7 x 10® 1.0 x 10® 1.82 5.3 x 10’ 2.0 x 10® 3.83 1.5 x 10’ 4.0 x 10® 26.74 • 3.8 x 10’ 1.0 x 10® " 2.65 2.8 x 10’ ND c ND6 2.1 x 10’ 3.0 x 10® 1.47 1.7 x 10’ 1.0 x 10® 5.98 1.8 x 10’ 3.0 x 10® 1.79 2.1 x 10® 1.0 x 10® 0.5

10 9.6 x 10! 1.0 x 10® 1.011 4.4 x 10’ 3.0 x 10® 0.712 1.1 x 10’ 2.3 x 10® 20.913 5.2 x 10’ 4.0 x 10® 7.714 4.2 x 10® ND ND15 3.0 x 10’ 1.0 x 10® 3.316 6.7 x 10’ 2.0 x 10® 3.0


-125-

Table 3-22. Survival rate for zinc(400 ppm) in site B

Site Total No. Survival No. Survival rate(%)1 9.3 x 10“ 6.9 x 10% 74.22 2.2 x 10® 2.6 x 10® >100.03 8.7 x 10% 6.7 x 10% 77.04 1.3 x 10® 1.5 x 10® >100.05 3.2 x 10% ND ND6 5.1 x 10% 4.9 x 10% 96.17 2.1 x 10% 1.3 x 10% 61.98 8.2 x 10% 6.5 x 10% 79.39 1.7 x 10% 2.3 x 10% >100.010 1.8 x 10® 1.5 x 10® 83.311 2.1 x 10% 1.5 x 10% 71.412 2.1 x 10% 4.5 x 10% 100.013 ■ 2.9 x 10® 2.2 x 10® 75.914 4.7 x 10% 4.2 x 10® 89.415 3.1 x 10% 2.2 x 10% 71.016 1.1 x 10® 6.3 x 107 57.3


Table 3-23.. Survival rate for lead(500 ppm) in site B.

Site Total No. Survival No. Survival rate(%)1 5.2 x 10% 5.1 x 10% 98.12 4.1 x 10® 5.5 x 10% >100.03 4.0 x 10% 5.6 x 10% >100.04 1.9 x 10% 1.3 x 10% 68.45- 4.5 x 10% 3.4 x 10% 75.66 2.7 x 10% 1.9 x 10% 70.47 2.2 x 10% . 2.2 x 10% 100.08 2.4 x 10% 3.4 x 10% >100.09 - ' 1.7 x 10® 1.6 x 10® 94.1

• 10 2.5 x 10% 2.3 x 10% 92.011 3.5 x 10% 3.7 x 10% >100.012 1.2 X 10% 1.5 x 10% >100.013 1.4 x 10% 1.1 x 10% 78.614 ' 1.7 x 10% ■ 2.3 x. 10% >100.015 . 1.8 x 10% . 1.8 x 10% 100.016 . 1.1 x 10® 9.9 x 107 90.0

Average 91.7

-126-

Table 3-24. Survival rate for pentane(20 %) in site B.

Site Total No. Survival No. Survival rate(%)1 4.3 X To! 4.0 X 10% 0.92 4.5 X io! 3.9 X 10= 0.93 1.7 X io! 1.0 X 10% 5.94 4.6 X io% 6.8 X 10% 14.85 4.8 X 10% 8.4 X 10% 18.06 1.2 X 10= 1.2 X 10% 10.07 5.6 X 10% 2.9 X 10= 5.28 2.6 X 10= 7.0 X 10% 2.79 1.3 X 10= 1.8 X 10% 13.910 3.9 X 10= ’ 1.2 X 10% 3.111 2.4 X 10% 6.6 X 10= 27.512 1.3 X 10% 8.7 X 10% 66.913 4.7 X 10= 1.5 X 10% 3.214 6.1 X 10= 6.8 X 10% 1.115 1.5 X 10= 1.7 X 10% 11.316 8.0 X 107 3.0 X 10= 3.5

Average 11.8

Table 3-25. Survival rate for hexane(20 %) in site B.

Site Total No. Survival No. Survival rate(%)1 4.2 x 10% 1.0 x 10% 0.22 3.9 x 10= 1.9 x 10= 0.53 3.2 x 10= ND 7 ND4 . 1.3 x 10= 3.8 x 10% 29.25 1.3 x 10% 5.0 x 10= 3.9 .6 5.1 x 10% 4.3 x 10= 8.47 4.8 x 10% 5.3 x 10= 11.08 1.8 x 10= 5.9 x 10= 3.39 2.3 x 10= 1.5 x 10% 6.510 2.8 x 10% 1.4 x 10% 5.011 2.5 x 10% 3.5 x 10= 14.012 2.1 X 10% ND „ ND13 3.4 x 10= 5.8 x 10% 17.114 6.8 x 10= 2.1 x 10% 3.115 1.2 x 10= 7.9 x 10= 6.616 1.3 x 10= 5.0 x 10= 3.9

Average 8.1SfD : not detected

#444 ^7] 41 ^4 4 444 4 4 44 44^^4=^

7} 4f-44 4 #4# ?M4.

-127-

3-2-5. C 44 5&4I54 ##14 #415o1| 4#

4 41 7fl#S# 7>57> ##4&4 45-S 4 4# #5 4 #5

5. 1^4 4 4 55(Chough, 1993), 7}i7} ##4$14 1 44 # ### §32]IS @4 45.1- 1 $$5, 444 4

#4 14# 20-30m 4:455. 54451 4444 !#!4 44

15 444 4# 4### 24444(Tables 3-26 to 3-33). C

4 4 144 !#!4 44 4e#4 Ni4 87.8%, Co4 79.8%,

Cd7} 87.5%, Hg7> 7.0%, Zn4 84.2%, Pb7> 47.7%# 5^5 441

54 44 4 44.3%, 444 36.2%4 41## 444 4 Pb4 41

Co# 4445# A, B, D 444 44 14455 l#44 441

54 tfl< 454 &# 41## 4# 455 244^4. 4 4#

4 4#4 15 4 455 144 4 444 111 #14=4 4^4#5, 2 7>57> ##4 7j55 54 44#52 4—45 ##47]

4)#44 4444.

3-2-6. D 44 54414 #114 Ei415oi|

5###5444 44 4455 445# #1# 44 100 - 150m

#455 # 30 4 444 54# 44ft d 44# 54##&4#4

4 4 4 44 15 4 455 #44 4##45 444 41## B 4 4

4 ### 4 4, 44, 444 5445 4# 44#44(Chough,

1993). 4 D 4 4 54454 #414 4415 444 4# 4###

24-4 44(Tables 3-34 to 3-41), NH 4# 4141## 71.8%, Co

# 76.0%, Cd# 85.9%, Hg# 1.2%, Zn# 79.6%, Pb# 88.3%# ^45

44154 #44 44r# 44 12.0%4 3.5%4 4### 44444.

0} D 444 ##14 44-154 4# 4141## A 4 44 45#

W; ;rc.

—128—

Table 3-26. Survival rate for nickel(600 ppm) in site C.

Site Total No. Survival No. Survival ^rate ( %

1 ' 3.7 x 10" 3.3 x 10® 89.22 7.3 x 10® 7.4 x 10® >100.03 2.6 x 10% 1.8 x 10% 69.24 8.5 x 106 7.9 x 106 92.9

Average ' 87.8

Table 3-27. Survival rate for cobalt(500 ppm) in site C.

Site Total No. Survival No. Survival rate ( %)

1 5.1 x 105 3.7 x 10® 72.52 3.1 x 107 1.7 x 10% 54.83 2.4 x 10%

6.1 x 1062.2 x 10% 91.7

4 8.7 x 106 >100.0Average 79.8

Table 3-28. Survival rate for cadmiumClOO ppm) in site C.


1 . 2.2 x 10® 3.4 x 10" >100.02 8.8 x 10® 1.1 x 10% >100.03 1.6 x 10% 2.0 x 10% >100.04 1.5 x 107 7.5 x 106 50.0

Average 87.5

Table 3-29. Survival rate for mercury(20 ppm) in site C.


• 1 2.3 x 10® 4.0 x 10" 17.42 1.0 x 10% 1.0 x 10 1.03 1.7 x 10% 4.0 x 10 2.44 1.1 x 107 8.0 x 105 7.3

Average 7.0

-129-

Table 3-30. Survival rate for zinc(400 ppm) in site C .


1 2.8 x 10“ 2.1 x 10“ 75.02 1.2 x 10% 9.9 x 10% 82.53 2.1 x 10% 2.0 x 10% 95.24 7.5 x 106 6.3 x 106 84.0

Average 84.2

Table 3-31. Survival rate for lead(500 ppm) in site C.


1 3.5 x 10% 1.4 x 10“ 40.02 1.1 x 10% 6.3 x 10% 57.33 1.4 x 10% 7.1 x 10% 50.74 6.8 x 10s 2.9 x 10s 42.6

Average 47.7

Table 3-32. Survival rate for pentane(20 %) in site C.


1 3.2 x 10“ 1.5 x 10% 46.92 4.8 x 10% 1.9 x 10% 39.63 1.1 x 10% 6.9 x 10% 62.74 6.4 x 10“ 1.8 x 10“ 28.1

Average 44.3

Table 3-33. Survival rate for hexane(20 %) in site C.

Site Total No. Survival No. Survival rate ( % )1 • : 2.3 x 10% ■ 1.2 x 10“ 52.22 9:4 x 10% 3.5 x 10“ 37.23 1.0 x 10% 1.6 x 10® 16.04 5.1 x 10® 2.0 x 10“ 39.2

Average 36.2

-130-

Table 3-34. Survival rate for nickel(600 ppm) in site D.



No.Survivalrate(%)

1 1.50 X 10° 7.80 X io; 52.0 16 3.14 X 10° 1.48 X 10° 47.12 3.50 X l0O 2.00 X 10Z 57.0 17 3.80 X io8 3.80 X IO8 100.03 6.05 X io? 6.11 X 108 >100.0 18 3.74 X 108 2.25 X 108 60.24 1.74 X 101 1.31 X 10 75.3 19 2.94 X 108 2.52 X io8 85.75 8.71 X 108 7.62 X 10Z 87.5 20 4.02 X 10" 2.18 X 10" 54.26 2.72 X io? 2.17 X io8 79.8 21 2.53 X IO8 1.88 X 108 74.37 6.68 X io? 3.68 X io8 55.1 22 1.74 X 108 8.10 X 10" 46.68 2.04 X 10? 1.65 X io8 80.9 23 6.27 X 108 5.23 X io8 83.49 4.38 X 10Z 2.47 X 10Z 56.4 24 1.01 X 10? 1.12 X 10? >100.010 3.09 X 10Z 1.90 X 10Z 61.5 25 7.10 X 10" 4.70 X 10" 66.211 5.12 X io? 3.78 X io8 73.8 26 1.68 X 10? 1.60 X io8 95.212 2.07 X 10o 1.04 X 10" 50.2 27 2.31 X 108 1.82 X io8 78.813 2.93 X io? 2.50 X io? 85.3 28 5.20 X 108 5.10 X io8 98.114 3.30 X 10O 1.65 X io8 50.0 29 1.21 X 10" 1.04 X 10" 86.015 5.63 X 108 2.48 X 108 44.1 30 1.60 X 108 1.12 X 108 70.0

Average 71.8

Table 3-35. Survival rate for cobalt(500 ppm) in site D.



No.Survivalrate(%)

1 1.38 X 108 7.10 X 10' 51.5 16 2.15 X 10a 1.76 X 10ti 81.92 3.62 X 10" 1.93 X 10" 53.3 17 1.20 X 108 2.10 X IO8 >100.03 8.86 X 108 5.53 X 108 62.4 18 1.47 X 10" 1.39 X 10" 94.64 7.80 X 108 6.31 X 106 80.8 19 3.11 X 108 1.51 X 108 48.65 1.17 X 108 7.10 X 10" 60.7 20 2.68 X 10" 1.92 X 10" 71.66 1.94 X 108 1.81 X 108 93.3 21 1.37 X 108 2.09 X 108 >100.07 2.57 X 108 2.16 X 108 84.1 22 6.50 X 10" 3.60 X 10" 55.48 2.66 X 108 2.11 X 108 79.3 23 3.24 X 108 2.87 X 108 88.69 3.90 X 10" 6.00 X 108 15.4 24 1.15 X 108 1.27 X 108 >100.0 .10 3.28 X 10" 2.11 X 10" 64.3 25 4.60 X 10" 8.40 X 10" >100.011 3.15 X 108 1.95 X 108 61.9 26 5.83 X 10" 7.00 X 10" >100.012 1.90 X 10" 1.30 X 10" 68.4 27 1.87 X 108 2.48 X 108 >100.013 5.30 X 10" 7.10 X 10" >100.0 28 5.00 X 108 3.50 X 108 70.0 •14 2.76 X 108 1.85 X 108 67.0 29 2.27 X 10" 1.88 X 10" 82.8 •.15 1.01 X 108 4.40 X 10" 43.6 30 7.50 X 10" 1.37 X 108 >100.0

Average 76.0

-131-

Table 3-36. Survival rate for cadmium(100 ppm) in site D.



No.Survivalrate(%)

1 1.98 X 10! 1.47 X 10! 74.2 16 3.49 X 10! 2.77 X 10= 79.42 2.99 X 10l 2.84 X 107 95.0 17 2.70 X 10= 2.70 X 10= 100.03 5.24 X 10= ND ND 18 1.63 X 10= 1.48 X 10= 90.84 1.20 X io! 1.13 X 107 94.2 19 5.62 X 10= 5.95 X 10= >100.05 1.19 X 10= 1.08 X io! 90.8 20 6.10 X io7 7.70 X 107 >100.06 1.33 X 10= 1.03 X 10= 77.4 21 4.93 X 10= 3.95 X 10= 80.17 3.91 X 10= 2.83 X 10= 72.4 22 1.66 X 10= 1.43 X 10= 86.18 2.60 X 10= 2.22 X 10= 85.4 23 4.58 X 10= 3.67 X 10= 80.19 6.70 X io! 7.60 X io! >100.0 24 2.77 X 10= 2.75 X 10= 99.310 6.30 X 10O 7.90 X io7 >100.0 25 4.90 X 107 1.90 X 107 38.811 5.71 X 10= 4.88 X 10= 85.5 26 1.97 X 10= 1.61 X 10= 81.712 1.33 X 10O 1.21 X io7 91.0 27 6.13 X 10= 7.47 X io! >100.013 5.67 X 10= 5.64 X 10= 99.5 28 1.73 X 107 1.56 X io7 90.214 4.53 X 10= 4.20 X 10= 92.7 29 1.37 X 10= 1.27 X 10= 92.715 3.32 X 10= 3.94 X 10= 100.0 30 9.20 X 107 9.90 X 107 >100.0 -


Table 3-37. Survival rate for mercury( 20 ppm) in site D.

Site .Total No. SurvivalNo.


No.Survivalrate(%)

1 1.65 X 10! 1.00 x 10= 0.6 16 3.50 X 10= 3.00 x 10 0.12 4.10 X io7 ND ND 17 4.00 X 10= 1.00 x 10= 2.53 4.23 X 10= 1.00 x 10= 0.2 18 2.16 X 10= 3.00 x 10= 0.14 1.61 X 107 1.00 x 10= 0.3 19 2.79 X 10= ND ND5 1:19 X 10= 4.00 x 10= ND 20 3.99 X 107 ND

10=ND

6 1.62 X 10= ND10=

0.3 21 2.29 X 10= 7.00 x 3.17 3.30 X 10= 1.00 x ND 22 2.58 X 10= 1.00 x 10= 0.18 5.14 X 10= ND 0.4 23 5.47 X 10= 1.00 x 10= 0.19 2.44 X io! 1.00 x 10= ' ND 24 1,73 X 10= 1.20 x 10= 0.7

10 2.80 X 107 ND 0.1 25 7.80 X 10 8.00 x 10 10.311 6.10 X 10= 7.00 x 10= 3.6 26 2.77 X 10= 1.00 x 10= 0.112 8.40 X io7 3.00 x 10 ND 27 5.32 X 10= ND ND13 4.32 X 1.0= ND 0.2 28 5.10 X 10= ND ND •14 5.16 X 10= 1.00 x 10= 0.2 29 1.81 X 10= ND ND15 2.34 X 10= 5.00 x 10= 0.2 30 1.54 X 10= 1.00 x 10= 0.1


-132-

Table 3-38. Survival rate for zinc(400 ppm) in site D.



No.Survivalrate(%)

1 1.43- X 10! 9.50 X io; 66.4 16 2.81 X 10= 9.40 X 10' 33.52 4.29 X 1° 3.87 X 10 90.2 17 3.41 X 10= 3.00 X 10= 88.03 5.66 X io! 5.79 X 10= >100.0 18 1.99 X 10= 1.77 X 10= 89.04 1.85 X 10" 1.20 X 10" 65.0 19 1.97 X 10= 2.40 X 10= >100.05 7.79 X 10o 2.81 X io" 36.1 20 3.10 X 10" 9.90 X 10= 32.06 1.85 X 10= 1.67 X 10= 90.3 21 1.23 X 10= 1.52 X 10= >100.07 1.88 X io! 3.40 X 10= >100.0 22 1.60 X 10= 1.17 X 10= 73.18 1.69 X 10= 1.82 X 10= >100.0 23 3.47 X 10= 3.15 X 10= 90.89 3.80 X io! 4.80 X io! >100.0 24 2.84 X 10= 2.56 X 10= 90.110 4.70 X io" 3.00 X 10" 63.9 25 2.11 X 10= 7.90 X 10" 37.411 3.86 X 10= 3.14 X 10= 81.4 26 1.23 X 10= 1.02 X 10= 82.912 4.30 X 10" 4.20 X 10" 97.7 27 5.43 X 10= 5.27 X 10= 97.113 5.70 X 10= 5.72 X 10= 100.0 28 1.11 X 10" 6.60 X 10= 59.514 6.20 X 10 1.79 X 10= >100.0 29 1.55 X 10= 5.40 X 10" 34.815 3.83 X 10= 4.22 X 10= >100.0 30 1.92 X 10= 1.68 X 10= 87.5

Average 79.6

Table 3-39. Survival rate for lead(500 ppm) in site D.



No.Survivalrate(%)

1 1.84 X 10! 2.02 X 10! >100.0 16 3.02 X 10= 2.31 X 10= 76.52 2.89 X 10" 2.18 X io! 75.4 17 5.30 X 10= 4.70 X 10= 88.73 3.41 X 10= 3.27 X 10= 95.9 18 3.11 X 10= 3.40 X 10= >100.04 1.63 X 10" 1.37 X 10" 84.1 19 3.32 X 10= 3.72 X 10= >100.05 1.28 X 10= 1.38 X 10= >100.0 20 1.16 X 10= 1.12 X 10= 96.66 8.30 X 10" 8.90 X 10" >100.0 21 3.02 X 10= 2.06 X 10= 68.27 3.86 X 10= 3.96 X 10= >100.0 22 2.31 X 10= 2.34 X 10" >100.08 4.21 X 10= 4.59 X 10= >100.0 23 5.88 X 10= 5.63 X 10= 95.89 4.13 X 10" 3.87 X io! ,93.7 24 1.21 X 10= 1.02 X 10= 84.310 9.10 X 10" 5.80 X 10" 63.7 25 7.50 X 10" 2.40 X 10" 32.011 3.81 X 10= 3.78 X 10= 99.2 26 2.95 X 10= 2.11 X 10= 71.512 1.72 X 10" 1.67 X 10" 97.1 27 6.32 X 10= 5.94 X 10= 94.013 5.68 X 10= 5.34 X 10= 94.1 28 8.80 X 10= 6.30 X 10= 71.614 4.53 X 10= 3.35 X 10= 74.0 29 8.90 X 10" 8.30 X 10" 93.315 2.76 X 10= 2.89 X 10= >100.0 30 2.62 X 10= 2.62 X 10= 100.0

Average 88.3

-133-

Table 3-40. Survival rate for pentane(20 %) in site D.



No.Survivalrate(%)

1 1.96 X 10= 4.10 X 10% 20.9 16 3.03 X 10= 7.00 X 10= 2.32 1.11 X 10= 1.00 X 10% 9.0 17 5.60 X 10= 3.50 X 10= 62.53 3.87 X 10= 3.00 X 10= 0.8 18 4.32 X 10= 2.00 X 10= 0.54 2.05 X 107 1.70 X 10= 8.3 19 2.10 X 10% 1.00 X 10= 4.85 8.00 X 107 2.00 X 10= 2.5 20 1.37 X 10% 8.90 X 10% 65.06 8.20 X 10 1.70 X 10% 20.7 21 2.11 X 10= 2.50 X 10% 11.97 6.23 X 10= 1.00 X 10% 1.6 22 2.79 X 10% 1.60 X 10% 5.78 5.32 X 10= 2.63 X 10= 49.4 23 6.50 X 10= 6.00 X 10= 0.99 6.33 X 107 3.40 X 10= 5.4 24 1.17 X 10= 1.20 X 10% 10.310 5.31 X 10 2.10 X 10= 4.0 25 1.16 X 10= 1.60 X 10% 13.811 3.11 X 10= 1.40 X 10% 4.5 26 1.03 X 10= 1.00 X 10% 1.012 6.40 X 10% 3.00 X 10= 4.7 27 5.55 X 10= 1.00 X 10% 1.813 7.34 X 10= 3.70 X 10% 5.0 28 6.10 X 10= 1.80 X 10% 29.514 3.55 X 10= 5.00 X 10= 1.4 29 2.78 X 10% 8.00 X 10= 2.915 3.40 X 10= 9.00 X 10= 2.7 30 1.56 X 10= 9.00 X 10= 5.8

Average 12.0

Table 3-41. Survival rate for hexane(20 %) in site D.

Site TotaLNo. SurvivalNo.


No.Survivalrate(%)

1 2.19 x 10% 3.00 X 10= 1.4 16 3.57 X 10= 2.00 x 10= 0.12 3.30 “x~ 10% 1.00 X 10= 3.0 17 4.90 X 10= 1.00 x 10= 2.03 4.58 x 10= 2.00 X 10= 0.4 18 2.35 X 10= 1.00 x 10= 0.14 2.50 ix-10% 1.00 X 10 4.0 19 4.36 X 10= 8.00 x 10= 0.25 2.35 x 10= 1.00 X 10= 0.1 20 3.20 X 10% 5.70 x 10= 17.86 1.27/ x, 10= 4.50 X 10= 3.5 21 1.76 X 10= 3.00 x 10= 0.27 4.06 x 10= 1.00 X 10= 0.3 22 9.20 X 10% 1.20 x 10= 1.38 5.811 x. 10= 1.00 X 10= 0.2 23 3.87 X 10= 6.00 x 10= 0.29 1.38 x 10= 3.40 X 10= 2.5 24 1.97 X 10= 3.30 x 10= 1.710 1.91 x 10= 1.30 X 10 0.7 25 1.20 X 10= 6.20 x 10= 5.211 3.98? x' 10% 5.00 X 10= 0.1 26 1.14 X 10= 8.00 x 10= 7.012 1.56, x. 10% 3.30 X 10= 21.2 27 3.97 X 10= 6.70 x 10= 1.713 7.38-Xx 10= 2.00 X 10= 0.1 28 7.40 X 10= 1.90 x 10= 25.714 2.95 x 10= 1.00 X 10= 0.1 29 1.04 X 10= 6.00 x 10= 0.615 6.45 x. 10= 1.00 X 10= 0.1 30 1.34 X 10= 3.00 x 107 2.2

Average 3.5

-134-

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-135-

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Residue gas (C1-2)

Natural gasoline (C4.7)Liquid Light end

Condensate (C4+)Stabilization

Sulfur Plant

GatheringLine

Well Heads

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Dehydration and Liquid Recovery

Figure 4-1. Schematic of typical gas processes

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Unmeasured Location

Figure 4-2. Statistical point of view

4 €4, €#€ 44# ##€% 4##4 #4 (reservoir characteri -zation)# 7}#% € €%%7)) 444# 444. 32] 4 7]### #4€ #€ %# 44% 444€# 4444 #M% 44& 444 444. 4 #€44 tSlt 444 4444 4# €44 3.4 37>4 4445. 4

4 # $14. 4, 44 (sedimentary), 44 (diagenetic), 3 2] 3 43:44 (tectonics)44°14. 44 444 44 "94# 44 44# €4 444# 35 .44-44 44 444 44 4# 344 45.7)1 4# €44 €4. # 4] 44# 444 4-f-4€ 44 #4 444 €44 44 444*4 441 ### €#44 444 34# €444 24 #4# 44445 44, # 4i.oi 14- <£40] 4# *o)l 44# €4, €4#3.s] #5444 444 7)1 4# €€4 €4.44) 44# 4#4 4# 3# #44 #44# 4 4 4 #4 €€4 44 4##4 #444 #4 32)3 4% ## #4 €# 44##4 44# €4 €44 .44 4#€4 #%4 €4# €€4 €4. 44% €€#4 €4 4## ## (#4# ^ ### #)# €44 44 ##€44 44, 35)€ ##€# #443 4€ #34€ €444 4 €#44 #444) €4. 444 4##€ W# #€€ #3# 4# #4 €# 443 44# 4= €4. 44 45-44 €€4# €4 45.7} € 4% 4#4 tf)% 712l €4 €%# €# %#7} €4 (A.J. Sultan 1989).

*M 7]#^4 4-4 #4# 3#47) €4)4# #€€ 444 ##4 % 4## #&# %4 4^44 4# 4#4 €4 444 %#4, 4# €% 4 €4 €4# #€€ €€ €44 ## (#4-3)# 4€ #4% 44# 44# # €# #4%€ €35 €#444 %4. 3# 4#4 4 €# 4*# #5 %# (probability density function) %#5 5.444 4 %4. 4714 4# #3 %## #44 €44 €4 #44 #€€ €# €4 44## 4# €4)4€ €3# 44%4. 4€4 ## # 7}4 #4# 4€%7]

146-

4# Warren^ Price (1961)€ 444 ## (€4-5)& 4 4444 €4#<LS if444 54€54 444 4€ 4 5444 4W £€ 4# 4 4 (correlation length)4 7TN& 54 4—5.4 44444. 2144 4 4 3M4€ 44 54€5<>11 44 €4€ 4 €4 44 &&54, n 44 4 444 4iz 44 ## (€45)4 44 €54 44 4 4414 45* 4 4 Warren4 Price (1961)7} 444 4 4444 44 #& (€45)4€ €44 445 7}4431, 4444 4^47} €4 €5 €44 444 #4 (454 £4 €4)4 44 4€€ 444 7}4 44, 444 444 44 €4 4:4 M455 44 # € 44 4^4 (A.J. Sultan et al. 1990).

4# 45 €5 (probability density distribution)4 4 4 £5. 5.44 €45-4 44 :o4€54 44 ^44 Greenkem4- Kessler (1969)4 °1 €££€4 54 444. >, 4# 45 4€i 44 54 4 € $1€#4 €4€ €544 (heterogeneity), 4 5€4 (nonuniformity), zis)5 4 44 (anisotropy)€55 4# € $14- 4# #4, 544 4)4 (uniform media)4-^ ,4€'444 44 (€4-5)5 44 444 4€ 9:55.544 7}€44€ 4€ 44 44. ^.44 44144 €4€4 44 PDF(probability density function)€ 44 €44 444 k444 4444. °1 44 4°) 444 5€4 (uniform)€ 444 € &€ 4€€ Dirac €4 4€4 44, d(&)5 57] 44. Dirac 447} €44444, 5.44 €4

444 4445, 5.7} 4€44°ll 5444, 444 44 4€ %

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-147-

(1).FU5D=SrA«1=1

47]*| N* 4*4 *44, £V* 4* 7]]* (weight coefficient)^. *

* 31* 4444 4*4 4* 14 44. 44# *444 47} Fig. 4-34

4 # ^445 4*4, 0i Si4 ^** 5*# 44 (uniform media)44,

5*# 4 tM^4 4* *^* *4 #-#5 444. 444. 44 5*A544 (homogeneous media)4 4* 44 444 44 #*7} #4431, #

5## 44 (heterogeneous media)4 4* *4 4 44 44 **7} #4

4* 4* 4 # 44. * 44 #** Greenkom4 Kessler(1969)4 44

u]5*^ 44 (nonuniform media)7} ^4 4 4*4, 4* 4444 #4

#*5# # * 4* 4# 45. #* (probability density function)5.

4 44* ^4 444. 45*4 4444 5*4* 444 4*^5.4*5 M4 7}*4 44, 4445 15144 445 44 44-4

4* *5 #45 s# 44** * * 44 (Fig.4-3).4 44 #* *34** *44 4## (random variables)4 *4 *5

# #3144 4*4 4444 44# 4554, 444 4** 4&5 44 *54 4**4 *4 4-4* 4* 5*4 4* 4$14.

2-2. *4IB4 51 (stochastic model)

244 4444 45 444 444 44 *4 4:* 43.7} 5£#4 *4 444 44 7144: (expected values)* *4* 444. 4* #4, 45 444 43-47} #4 *4# 4*. ** 45* 4*4 4*44 *4 44 5* 4444 44 *4. 44* 455 4444 4*4 *4 (4 2).

148-

%

jiI

'\

Figure 4-3. Probability density function for parameters (after Greenkorn and Kessler 1969)

Z

(2)m= 2-^- s £[ «/]

444 (=) 5E4# #e 44# #A13-*\ 4&4 t7> #44 #4 s) 4 44% £[ y,] # TO%4 44# 4# 4444. zLSjjL 444 #

#4 tfltb #4 %# 44 4# m 4# variogram X^)4 4#44- 4 7] "4 variogram# 45. 444 444 4# 444 #4(correlation length)# 4#47l 4^0 4#44. 44] 4-5-# 4 #44 24 -# 444 ## 4-S-4 44 4441# #4471 44 variogram4 #### 4 #54 (experimental semivariogram) r*(/z)6l #S 4#4#4, 31 3}

# 4 as 4444 4#4 #4 (43).

/*(/&) =z(,x) — z(x+Ji)\ 2............................(3)

4447* (A) : Experimental semivariogram

z(x) : x#444 4S4 ' z(x+h) : x#44 h 4# #44 4444 4&4

n : 447> ## 4a ##4 #4a444 444# #44 44 44 4-4 4 3# 4 #44 44 4

4 h## #44 4&4#4 44##4( r*U))# #4a, 4#4 4&44

4 44 2hi 44 44M4# #44 tl^ji #4 4s4#4 44# 3h, 4h4 #7>447>4 #7># 444 4# #1##%# #44 444 #4# &r# x## 444 fAS 431 y## #^##%as 44 s44 4 (Fig 4-4). #44as. ##### 4#4 4444 #44 4#4 #44# 44^as 4#44 44 4#####& 44

-150-

Semivariogram

SiH - C + Co

Co : Random variance

a - Ranges of influences

Separation distance(h)

Figure 4-4. Three-parameters determined from semivariogram.

(fitting)*^) #4. ###38# 4##3##2 #4 #4# 4# 4# 4# #2# (trial and error), 434## (least-squares), Gauss-Newton, ## 4# (linear-tangent)## 4# ##22 4#47l^4, 484 7>4 %4 52.51^ 444 844, #44-5-2 ###2 ### Wt2! #44 44471 4#4 4442#4 ^5. 4444 (444,1992). ###284 4##2#*#2 444 4# 4444 #8## (spatial variance) 444 44 (random variance), #44 (sill), zl^zl ### (range of influence)>4 #91' 44 (Fig4-4). 444 4414 44#### #24# .4444 44 544 4#91444 .4444 28# 444 4 ^#24 4# 44 4 44 822 ##84.

2-3. sl4 ##

2-M4 ##4# 44444 44# 4a^l 444 4% 42# 44 444 44 4544 4#91444 544 4#9144-5-3. 4# 4 88. 4544 4#9144# 44 44# 444 444 43.4:4 434 44 4# 4-484 44 444 2#8 444 4# 444 #4 (4#4 ##) 4 4# 4## #4# 4444 ##8 #4 844. 444 #58# 4 #91444 491 ##8 4##8#4 8# #914 #44 4444 #4 4fHF4 4'3#2. 4# 444 44 #44# 44 4# #4 84 4# € # 44. 4# #4, #54# 4#9144 444# FFTM (fast fourier

transform method), SPM (source point method), ZL^ZL TBM (turning

bands method) 44 #4 84. 4 4444# 4# #44 FFTM###4-#44 4##8 #48# ##484. #4, 54# 4 #914 4# 48. 4 91## #7} #32.4, 44# #4# 3# 48#4, 4# #54# 4

-152-

*44** FFTM W4 44 *#4 *##4 #* it* 3* *^0)1 4

#44 *#44. 4, 2^-t- **44** *54* *#44*4 #44 **(**, **)* #*#** 44 to safe 4&^ *** 5## *

&fe ** St# *#44 (A.J. Sultan et al. 1990).

2-4. £|S| #g SS S4fe*

#** 71**4 *## 4444 44 47i 4# 44 #4**471

# 4*44 16“xl6" 3.714 44 #454* 0.544 4:455. 32x32?H

44 44 *4** *4444 (Fig. 4-5). #4* to* 4*44 44 to si 4*4 #4* 4 44, 444 0.30744, 5*# to 0.5544 4*

-4**5E.(log-normality)7> 4444 (Fig.4-6). 4*4 4-^-4 4*4 *

57> 4 *-4**5 444 4* 4* *444 *4*4 4*4 *54-* *443 *4* 4* 4444. 34JL (experimental

variogram)* 4*44 4#*5.4*# 5## #4 *44*#* 04 3,

***** 0.35 44 4*4 sill St* 0.354 #4. 5# 4#** 10.155.

44)*4 (Fig. 4-7).

*7144 *44 47H#** *5** 4*51144* FFTM4 4*4 #4* Figure 4-84 #4- 3*3 ^4* 4*44*4 44 5*4 #4 fe Figure 4-94 *4.

4*4 4* #4* ##4 # nfl *5*# 4*44*4 44 *#* #4* 44 4444. *44414 4# **4 St47l 41*4 244 44 4* *1^1 oil 4# ##4 3444 tot## 54* 4*5114*4 44 4 *4 4*#4 ** St* Tlelsi #444* *# **# 4** 24€ .4 44 444 4# *** 45*71 4*4 *5#* *#4444 44 *

-153-

cn

Figure 4-5. Automatic minipermeameter apparatus

Number of samples

standard deviation

Gas permeability (md)

Figure 4-6. The histogram of permeability in slab(16"x!6")

Semivariogram

C ■ Spartial variance(£h 35) Co : Random variance (6.0} a : range of influenced. 1)

Separation distance (h)

Figure 4-7. Analysis of variogram (slab 32x32 point)

157

Permeability (md)

g 1.250 to <1.880

H 0.620 to <1.250

H 0.350to <0.620.

g 0.239 to <0.350

g 0.056to <0.239

□ 0.013 to <0.056

Figure 4-8. Permeability field generated by FFTM (unconditional simulation)

I

Z

inoo

Permeability (md)

■ 1.880to <2. 500

■ 1. 250to <1.880

0. 620to <1.250

0. 350to <0. 620

0. 239to <0. 350

m 0.056to <0. 239

□ 0. 013 to <0.056

11 0. 003to <0.013

Figure 4-9. Permeability field generated by conditional simulation

•-ha IKfS ^ agr

#-§-# ^"g-kWR) k kMlalHWirk Ms-to-fc k^k fc#-^k k>

-k-§-k -f-^k #kh Ma-h "h!o ^ # nrk# hkkk aws'k R#

#M3 M3ED3 344 44# 4# 44# e»#3 4 4 #44444. »«II4 43# 444# ss# e@114 1?$#

s., 3.4 JL lif?gmi§4 3444. 4 343# MmfI4 #3# 4444

444 SrSIK S343 #4, 44, 434 4^-4444 44 3#&

334s3 44. 43 JtrMIS 434 344 444 444# MJE4

3 4&31 3M- 4# 34 44 5.4s #A44. MM3 «34

SH 3&# 43 4444 43 4 ffl¥flr 35.71 a# 3334 143

34# 544 5# 334 343 443 4444.

3-1. ftpjg 3Sifs| S3

M mm# 443S4 4443S3 444 44#5 44 44. Hr Ml 434# 4443S3 *# 543 M. 44353 #s3 44 ammm* =la 33-4 44#5(aiiPhatic ho# 44# 44#5

(aromatic HC) 2713 5 4#4(Table 4-1). 34# 44#5# 43 443 44#5(alkane), 4143 44#5(alkene), 43143 44#5(alkyne), #43 4435(cyclic) 4 4713 & 4#4. e@mm3 44435# #, 3 5, 437>s, 1#, 443571 34#& 4344 IS, 4, 454 s#3 44#4' resin# asPhaltene3 54454, 3*71 431 41, 444##5 544s- 44.

444 443S(alkanes)# CnH2n+24 #33# 33 s 354 54 4 435 5# 4443 443555 #3 #4. 343 4435# 454 #7} #7144 44 #53, ##3, 4 #3 #714# 34# 4414.

-160-

(CnH2n+2) Paraffins, Saturated HC

(CnH2n) Olefins, Unsaturated HC

(CnH2n.2) Carbon triple bond

(CnH2n) Naphthenes

Alkyne

Alkene

Alkane

■— Aromatic

- Aliphatic

Cycloalkenes

Cycloalkanes

Table 4-1. Classification of hydrocarbons

Figure 4-104H 5# 44 41 ##1* 14471 4435 411 44 130°F4 #7l* S.e-14 4#*7l #7144! 44 *7l#l 4^41 l7fl

4#%# 4 35 - 55 °F1 ti]H4 444 #7}# 4444. ##14# 4 = 7ll 4444 444 #7l!## 3°H 4% 44.

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44144 44*#(alkyne)# 4#4 4#444 3# 44 *5* 7}

14 CnBbn-zl #714# *3 44. 44144 44### 444 1 4 144 44*#4 3 ##4 #7144. 44144 44*#! 441 4 #1* 444 44*#4 4144 44*#! *444.

4** 44*## 4435 451# 44*#5 4*# Ml 1 * #* 1* *5 43 44.-44! 1*5# 4#4 4#7MM 2# 44*

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-162-

Tem

pera

ture

(F)

-#— Boiling point

■0— Melting point

Carbon number

Figure 4-10. Physical properties of straight-chain alkanes

-163-

44 #4 4444 444^34 4444^34 44#3 44 $14 M -^15. n -§-37} 4444 (Table 4-2) 4 444 444 447}

^s. Ci44 Cg444 44& 7}431 $134, 7^44414^44 ^3 Ci44 Cie4444.

3-2. If* Sftta-21

3# «4 441- 4^44 44, 43, 41:44. *044

7}^b44 ^44^ 3444 7}it M444 43, ^4 4»444 444 43, 454 444 44 3 * (phase) 4 44 444. M44T 444 114' M-4 H?«fi4 44, 43, 4M 444 34, 7}3, 7}^4#4433 4¥444. 7}]4437} §}^ *044 7}^ 04

#4' 44 4#4 4444 4 4£4 44 44-434 Fig.

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44 7}^^#443 e*#4 43 e*437} 4443 444 ^44

7K#*#4 44.34 E*@4 ^4 117} ^3414 44 444 437} 4344 A3

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-164-

Table 4-2. Constituents of petroleum

CCNSmUBITS OF PETROLEUM

| NAME FCRMJLA BP(°Q USE

Methane c, -162 naturalgas

Ethane 0, -89 natualgas

Rnpane Q -42 natuaigas

Butane Q -5 natual gasoline

Pentane Q 32 natual gasoline

Hexane Q 63 natual gasoline

Heptane q 91 natual gasoline

Octane q. 118 natual gasoline

Decane C.0 174 motor fud

Tetradecane ^14 254 terceenefunaoedi

Hexacfecane C.6 287mineral dl funaoedi

Triacontane Qo 457liiaicab'ngdlfunaoedi

Tetracontane Qo 544liixicatingdlfunaoedi

Asphaltene 649<- asphalt

—165 —

Res

ervo

ir pr

essu

re, p

sia

4000 —|

Bubble Point or 1 Dew Point orDissolved Gas '! Retrograde

3500 — Reservoirs [ Gas-condensateReservoirs

3000 -CRITICAL j PT. |

2500 -

Single Phase Gas Reservoir

50 100 150 200 250 300Reservoir temperaure, f

350

Figure 4-11. Pressure-temperature phase diagram of a reservoir fluid

-Z9I-

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bio t # #-#-&b H"o"

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Table 4-3. Limits of system classification

LIMITS OP SYSTEM CLASSIFICATION

SYSTEMCOR

SQ7BB1.MOL %

ch4

.... WWLir

NATURAL GAS > 100,000 > 80 45 ~ 65

CONDENSATE 3,000 ~ 100,000 75 ~ 90 40 ~ 65

VOLATILE OIL 2,000 ~ 5,000 65 ~ 80 40 ~ 55

BLACK OIL 0 ~ 3,000 < 70 5-45

Pres

sure

GAS CONDENSATE

VOLATILE OIL

HEAVY OIL

Temperature ------- >

Figure 4-12. Phase diagram for typical reservoir fluid

Z

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-170

Pres

sure

Temperature

Figure 4-13. Pressure-temperature diagram *

/*

Liqu

id v

olum

e (%

)

80 —

60 —

20 —

Pressure (psia)

Figure 4-14. Liquid volume profile of oil reservoir

-172-

80 —

60 —

40 —

20 —

Pressure (psia)

Figure 4 15. Liquid volume profile of gas-condensate reservoir

-173-

3-3. A|SS| xH*|

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a#7)4 44& #444 424 4# 4s# 4447)1 44. 54 444

44 4#s. 44 444 4&# *}&*}*] #47)1 4 4# 4 ##55 4

#47) 444 2-37)1 444 4 a# 44444 44.

44 4s7fl#)A) #4# a>%o.s. 4#4 4# 44 44.

® A) 57)144(sampling line) 444 trap# #4# 4

© 457)144# 7y#44 #4# 44# 4

© 4 5 7)|44# 7>#4 4 #7)1# 4© 4a 7fl#)4 M4 1/344 ### 4444 4 s*'7)14# ^

© 500cc##4 stainless steel, carbon steel# a)-## 3)

© 45 7))#)|u #7)# 44 ^544 4#4#^© H2S7> Hf-4 7}AzA)S# ^4* 4##7)4 #5) 5# Ell#

€1S 554 #7l# A}## a

© H2S, C027> 544 71-54S# 4444 #4# 4© #44 27114 #714 #** 4#55. 4s# 44# 4

-175-

!B§H44 7>^Al5.d] ^y^l 7}^]^^, 414, 444*4

1 37>^17} $14". 71-^1444 (purging method by gas)-Sr a]5-§- 4#) 47]

4°)] o] B] 4 °)1 144 -S-7]# 7i)^y>317,> 4! 7>^hS. purging A] M 444

1 A) 5.1- ^44! 4444(?ig. 4-16). Purgingl! a] 52] 4^4 44

4144 4^4 ^111 4444. 45*1144 444 41 purging^! Table 4-44 4444. 2?M 1S.7> 444 14# Aj-g-1- 4141 4=

201 4s 7}Az& 4#A) 41 A) 5.1 4444 44. °1 444 441 1

4414 1*S3. 44 S.444. 444 is 4 444 4^44 #s#

14 440^ 44.

Table 4-4. Number of purging cycles upon sampling pressure

pressure (psig) purging cycle

15 - 30 13

30 - 60 8

60 - 90 6

90 - 150 5

150 - 500 4

over 500 3

. 4(evacuation method)! purging§14 444 7>a^a] 5.1 *]] 4 47]

4114 44 *44! §14. -t44 144 4144* 44 44444 44 4&4 4144* 1444 444 4544 44 4414 41 4s 44444. 444 4 44! rich gas4 454#] 4 a]5.4 1

S7> ±#!SS4 ^r! 414 4444.4 4 4#4 (liquid displacement by gas method)! 27]] 2] #5.7]- 44

-176-

Gauge

Sample Source■

Probe

ko=(l

Sample Line Separator

Inlet Valve Valve 2

Vent Valve Vale 2

l

SampleContainer

Extension |§llSi Tube Length lbs2 to 4 ft

3 C

Extension Valve Valve 4 Outlet Valve

Valve 3

Figure 4-16. Gas sampling layout for purging method (after GPA)

4 -0-7]* ** s* #3. 7]]*f 4* 7}^3 **A]7]* tij-^»]4. 4, *4^3 444 -0-7]a] ^ *ti5. 7>^* to44 §pf #

33 *** 4#444. 4 Mto ** £* #44 1*7>^* A}** 4" $14. 4to4 to## to 44 flx«fi 4^4 443-47} Fig. 4-174 44 $14.

44 44444 # 4&4 4# 4 3.*# 4 ##4 44 4toTable 4—54] &44 443 PVT* to 7>4 #* 44 EH437} *3

444**4 4444* m#§4& 7#* 444 44£-3. to $134 4

3$1* 4*;<M 444 ra@4 E=M®4 44 444 441*4 44 444 4* 4433 44 44 44447} 4*4 4&7]|^4 *44 4 44 444 344 to'to 3*7} #7] 41*44. a# 4144 «43 * 7}*44 #4 toto *4444 44 44 to 7}*34 44 4 4#'to 4 44444. =l 44* 4* 4* to4 4443 EH3*4 44-7} 4444 4to4.

Table 4-5. Volume of samples required for common tests

Analysis type Volume (cc)

PVT determination 280,000

Low temperature fractional analysis 140,000

Calorimeter test for heating value 85,000

Mass spectrometer analysis 280

Gas chromatography analysis 280

-178-

GaugeInlet Valve

Valve 3

Sample ValValve 1

Vent ValveVale 2

SampleSource

• SampleSample Line Separator Container

Probe

Outlet Valve Valve 4

Figure 4 17. Gas sampling layout for evacuation and liquid displacement method (after GPA)

3-4. ?\— 4.5.—I #4

7}2 2# 7}2ll4]o]E Mjf^l 2|7># 44 2.0} ^||

2#24 Fig. 4-183} Fig. 4-194] 44 44 $14. so} ##oj] 444#

2444, tKE #4 4# 7}224# 44, 7}^-21 4-4#4# 444 44 #244. EE1-44] $144# 4 #4 #4, 4-441 #4, 144# 44, 42444 #se}c}.

#444 7}2a]s.7} 2444 44 ##1 44# 44447] 444

4^ 140 °f 2# mm m$/M 24 20 °f #4 4-22 7}<s44°} 44. 444] 4 24-4 424-44 44 42421- 424-4 41-4 2# 7}2

# 42440} 44. 124 #444 42#4# 414% 4 #4] 1:2414

444 24?}4 #44 4-4# #44-4 ## 4## 1444. 44 42

44 #23} #41:24 #44 41 #44 $14 41 144 !#4 4

#4 44#2 44-22 7}##44 14. 4#4 14 44, 4414-7}

#41 7}4: 4&41 42# 4444 4444# 444 GC4 #414.

Fig. 4-194 44 44 14 7}24 21424 44 44# GC4 4 44 444-4 #1144 fifS WT4 mm2 4441] 14. 7}i42 4 4444# 14422 Ci44 Cell #444 2#42# 0444 C35 44 #447]] 44. C?+* 244 »44# 4#44 1 1# M44 4 44 444 #4444 44. 444 UrMSIt- 4 #44 flr^#2 144 4# 7121 #4(bubble point pressure)6]4 4#1 #4(dew point pressure)#- #4 44.

—180—

Fluid Analysis

Acoustic Velocity

Resistivity Index

Capillary Pressure

Formation Factor

Core Analysis

Porosity, Permeabi1ity

Sieve Anaysis (if unconsolidated)

Gas/Oil Relative Permeability

Residual Gas after Water-Drive

Overburden Porosity & Perm, (if unconsolidated)

Figure 4-18. Flow chart of reservoir rock analysis (gas condensate)

-181-

DEW-POINT/ RELATIVE VOLUME DETERMINATION

CALCULATION OF DATA

COMPRESSIBILITY FACTOR

FINAL REPORT

GAS VISCOSITIES

DEPLETION STUDY

EVALUATION OF DATA

QUALITY CHECK

SEPARATOR GAS/LIQUID SAMPLES

QUANTITY CHECK

SELECTION OF SAMPLES

SEPARATOR GAS COMPOSITION

EVALUATE WELL TEST AND SAMPLING DATA

SELECT METHOD OF RECOMBINATION

RETROGRADE LIQUID ACCUMULATION

SEPARATOR LIQUID COMPOSITION

Figure 4-19. Flow chart of reservoir fluid analysis (gas condensate)

—182—

4<g : mm ###

4 4o!]a^ «M! 444-71 4)4-4 4 3444 444MM! 4##4-& stfls 44 e@#4 ^444 #444 e^m# 4 44 44444 444 MM! 44-#£83 (P-T diagram) 44 -6- #44534. =l 4# 7>^MJ»! *#®4 7W> 44 #^4 444

14 #HE ##44 ZZ.4JL #^71-^44 4441 #4 444534.

4-1. 4##4 44

4 3444 #44 #471444 A<a4 7}^#! 4#& 7}4jl u® 444444 MM 4## 444534. 44 %A4 4s.# A!4 7>—4 #4 #0 4#(pseudo component)! ^-4 zisl JL #5]7l! 4! 4 #3, 7}3~344 #44. 4 #1-4 7)144 4#4 7}&# Table 4-64 44 #4 11711 4#A3. 4 #4 4 31A4 4## C7+ 471144.

Table 4-6. Composition of separator components

component C02 N2 Cl C2 C3 iC4 nC4 iC5 nC5 fC6 C7+

liquidmolefraction

0.39 0.23 12.55 6.71 10.04 6.34 8.37 6.21 4.63 8.67 35.86

gasmolefraction

1.39 2.33 78.03 9.13 4.98 1.50 1.52 0.52 0.33 0.27 0.0

GOR = 4,812 SCF/STB, separator pressure = 2,000 psia,separator temperature = 72 °F.

—183—

e»H C7+ 4^4 C7+4 4## 4 #4 0.774gm/cc»H #7}## 14044. 444 4&# 4-0-44 44€ HtWH4 4 #4 Table 4-74 44.

Table 4-7. Recombined composition

component C02 N2 Cl C2 C3 K% nC4 1C5 %C5 fC6 C7+mole.fraction 1.20 1.94 65.75 8.68 5.93 2.41 2.80 1.59 1.14 1.85 6.73

44 #4444 44# A#4 7>a4M 4# 7}A-A41k4 4,812 SCF/STB' tilirS. #4444 44 #4# 444 Br«II 4## Table4-84 44-

Table 4-8. Physically recombined composition

component C02 ! N2 Cl C2 C3 iC4 nC4 iC5 nC5: fC6 fC7molefraction 1.21 1.94 65.99 8.69 5.91 2.39 2.78 1.57 1.12 1.81 1.44

component? fC8 fC9 fCIO K%1 fC12 fC13 (C14 fC15 fC16 fC17 fC18molefraction 1.50 1.05 0.73 0.49 0.34 0.26 0.20 0.13 0.11 0.08 0.06

component fC19 C20+molefraction 0.05 0.15

44# 44 4## Table 4-74 4#4 44 44#4. 7}4 447} ## 4 #4 4#7}A4 C7+ 4#AS. 44 0.24 mole%4 0.14 mole%4

—184—

#47} 44 444 4** 44 44431 44. 444 4#44 314* *#*314 444 m®#4* 44 44#* 4 * 44.

4** 44444 #44 IfMlt 7}# 5 *4-*5# *S¥@i*31*4*4. *4-*5# 31#* 4 4 7}# #4##4* 4*4 ^4—4 7}# ££4 4*4* 4*4 4* Peng-Robinson *4*44* °1 *#44.

, = _____ ____________gLn__________p V - b ■ V{ V + b) + b{ V - b)

Fig. 4-204 444 44 #4 4 lf*®4 431*5* 152.6 °F43l 431*4* 3535.5 psiaS. 4**57} 200 °F4 7] 4*4 7}^44 45 #@#31 4**4 &7] #@#31314* s*31 «5 *3)#5 *4. 2*7] 44 44*4(cricondenbar)* 3545.83 ' psia°]4 44*5 (cricondentherm)* 390.135 °FS. 4444. 431*44 #4*44 44 * ##5 *4 4S<§ 7) 3}(retrograde vaporization)* *4471 *54 *31* 54 44*544 #47} 5714*31 4***(retrograde condensation)0!*44* *4* 4*4 W4. *4 2*4 *44* *47} W4 44# ffl*#* 444* If*®* #@*57} *°} 7}5** ^445 **4

4*4 *44* **4 *## #4431 44.

4-2. 4W (Constant Composition Expansion Test)

Table 4-831 *44 #4 If*®# **44 #5%* ^*44* * *34*4. ^#4#* if*®* mm #*-* *#4 7}* *4*##* *## 445 If* 54*^44 4*44 5-64 45 mm

—185 —

Pres

sure

(psi

a)

6000

4000 —

3000 —

2000 —

1000 —

Critical pointX

200 °F

i r-200 -100 100 200 300 400

Temperature (F)

Figure 4-20. Phase diagram of reservoir fluid

600

—186 —

44 ^1144 #414. 44 £! 4 #4# £4 £#4#£l £#4 44 Mif4 £7)#^ 4 7] £3 #?) WT444 U_L4 44, 7M= £# 7>^ 1144s em#4 3-Mir 44#- #@#4 £7)

444 4#1 44 444 €4. Fig. 4-214 4-2244 £# 44 44 # 44 44 #4 444 *414. 44 mm# 44 4444 44 4# 4 & 4"94 44 4#4 44441 14- #4 444 4M444 #4 44 444 4# 4# 4444# 44# # Sis# #4# 4 #4 444.& 4^ss. *44 # 7>dhffl4 44*4 2* E1S7> ^44 4s£#

##££ # 4-1-4 #44 44.^5%^ 14414 #44

0 £4444 440 £444 44444 m#4 4## #4 .(3) #47>s»] <g-#7))#(z) #40 £444 44444 £14 44# #4 #44. 444 4#

4&# 4 #-4-# 4441# #% mm #S7> #41444 4

44 4-5.4 ### 44144.Table 4-84 4# 0*114 4## C02, N2, .......... fC6# 1071)4

44444 #7114 C7+ 4#444(CCE-1 of Table 4-9).

44- 4#4 #4# 4,000 psiaS. 7>41 £ 6014.6 psia#4 187)) SH44

441 #)&# 4441# #1444. #1 #4# Table 4-104 44 Si

4. 4#1 #1# #14 41 4#1 414 3443 psia4 til H.40I 745

psi ## 3517.62psia£ 4414. 44# C7+ »4#°)1 444 44 4 4£5 4444.

11 1# ## 97)1 & #44 C02, N2, Cl, C2, C34 57)) #41 #4

C4-6, C7-10, Cll-16, C17+4 471) g§M#£S #4(CCE-2 of Table

-187-

Constant Compositon ExpansionOIL RESERVOIRe « m ' » . » ^ , w

Figure 4-21. Constant composition expansion (oil reservoir)

Constant Compositon ExpansionGAS CONDENSATE RESERVOIR

GAS GAS GAS GAS

Hg HgHg

•

Hg

Pi»Pb P2>Pb P3=Pb P4<Pb

Figure 4 22. Constant composition expansion (gas condensate reservoir)

Table 4-9. Input data of constant composition expansion calculation

<CCE-#1>

component C02 N2 Cl C2 C3 iC4 nC4 iC5 , nC5 fC6 ^7+

molefraction 1.21 1.94 65.99 8.69 5.91 2.39 2.78 1.57 1.12 1.81 6.59

<CCE-#2>

component C02 N2 Cl C2 C3 C4-6 C7-10 Cll-16 C17+

molefraction

1.21 1.94 65.99 8.69 5.91 9.67 4.72 1.53 0.34

<CCE-#3>

component C02 N2 Cl C2 C3 _ iC4 nC4 iC5 nC5 nC6 C7+

molefraction 1.21 1.94 65.99 8.69 5.91 2.39 2.78 1.57 1.12 1.81 6.59

<CCE-#4>

component Ci+N2 Cs+COa c3 % fCe C7-9 Cmmi C12-14 Cl5t

molefraction 67.93 9.90 5.91 5.17 2.69 1.81 3.99 1.22 0.80 0.58

<CCE-#5>

component COz C3+N2 c2 C3-5 Ce-io Cu+

molefraction 67.93 9.90 5.91 5.17 2.69 1.81

-190-

Table 4-10. Results of constant composition expansion test

* Constant composition expansion (CCE-1) %-'!<» » * * * :;<»t< * * * * * >!< * * * * * * * * £ * * * * * * % * % * * * * X * * * * * * * % * * * % * * * * * * * * * >:< $ * * * * * % * % *

Summary of Constant Composition Expansion at 200.00 degF Saturation Pressure = 3517.62 psia

P, psia relative oil gas gas liquid Ytot vol vis.cp vis, cp Z-factor mol % function

1 6014.70 0.8045 0.0000 0.00000 1.06372 5514.70 0.8289 0.0000 0.00000 1.00493 5014.70 0.8581 0.0000 0.00000 0.94604 4514.70 0.8941 0.0000 0.00000 0.88745 4014.70 0.9398 0.0000 0.00000 0.82956 3614.70 0.9867 0.0000 0.00000 0.7841

3517.62 1.0000 0.0000 0.00000 0.7733 0.0000

7 3442.70 1.0172 0.0000 0.00000 0.7750 21.1186 1.26798 3414.70 1.0238 0.0000 0.00000 0.7752 23.1093 1.26529 3364.70 1.0361 0.0000 0.00000 0.7753 25.1142 1.2604

10 3214.70 1.0757 0.0000 0.00000 0.7752 27.0181 1.245511 3014.70 1.1362 0.0000 0.00000 0.7755 26.9548 1.225012 2814.70 1.2075 0.0000 0.00000 0.7769 26.1289 1.203613 2414.70 1.3943 0.0000 0.00000 0.7842 23.8791 1.158314 2014.70 1.6721 0.0000 0.00000 0.7981 21.4128 1.109915 1614.70 2.1127 0.0000 0.00000 0.8195 18.8645 1.059116 1314.70 2.6432 0.0000 0.00000 0.8407 16.8878 1.019717 1044.70 3.4071 0.0000 0.00000 0.8637 15.0281 0.983418 850.70 4.2772 0.0000 0.00000 0.8827 13.6183 0.9566

-191-

4-9)-§>t11 44 °114 14 °1 3406 psiaS. 14 °1#4 14 54 37 psi 4

Til 4445 $14.

CCE-15 114^1 451 fees. 44 441-4 4511 444

l°-4 fC6 444 nC6* 4144(CCE-3 of Table 4-9) #S! 1144

I 4444 414 144 3517.62 psia44 3506 psiaS. 444 4444

54 45-41 4 1 $14. 444 4 4144 414 ie*Hl C6 4

II C64 454 141 444 5.4! nC6l 144 7>411 11$%

4.

444! 444 457}^# 4 4555 44 Table 4-941 41 44

14 W 451 414! 414 (CCE-4 & CCE-5 of Table 4-9) °11

4 114:44 3239 psia, 2909 psiaS. #41 °114 1144 41 44

41 1 1$14. 3 41! CCE-44 CCE-54 414 1441 (interaction

coefficient)! 444 414 1444 11 1444 $%7l 4)544.

444 41 571)1 #S1 4444 141 CCE-24 CCE-31 414

w mmmm 4-4it# #4144 4574 44 4-w 14» 5.14

(Fig. 4-23):. 44 7>41 45 511 CCE-27> 7>1 4444 CCE-21

45 511. 41 414s. 414. 14 IS! 41411 s4 &! 4

1 1444 (flash or equilibrium liberation)0) 435 44.

4-3. ### ME 44 (Constant Volume Depletion Test)

#S1 4444 4t)1 441 444 -S4 14444 4!#455. ### MM44°1 14. ##§# MM441 44 ME441 .2-4 si 7}di! 441 5.4 444>)] 5.447] 444 144! 444

4. ME 7)145 14 °1 45445 3.7)1 444 14 454 14144

-192-

4

0 1000 2000 3000 4000

Pressure

Figure 4-23. Relative fluid volume during constant composition

z

600

expansion

44 4W 444 #14* 7}$** *444 4 *441444 44& 44 4444 4444 44. 444 4 "84 44444 % #4 4#4 441 44. 4 #4* Fig. 4-244 $4* 44 44 $4 4444 4444 5 - 64 M444 444 7}$4 1*4 44 **

m@4 44* 4444 44.4 €"94 444 444* 4s* 4*4 #4.

1. $4*4

2. 4 *44444 *44 7}$4 4*4 *4*

' 3. z} «y-444444. 44S $4*

4 4(4)ffl4 (*)4ffi4 4$ '4 *4* 4 *44444 4** #44* **4 *4* 44& ffl¥E 4 444* 44 4444* 4* 4444. *4 4* #44 .2.4# # 47] 44 $* *** #444* 44 C7+* $44 MW 4*4 *44 *7>* #*4 #4 *444* 44.

4 **44* CCE-244 4*4 4*4 7}* jl ### MM4 4 *

*14*4. 44 144 7}$* 3414 *44* 444 44$4*4 $

*44 44 444 *444 *44 *$* 815 psia4 80 "F44 2*4

*B] 7] 4 *44 *$* 65 psia4 80 °F44- 44* *44* stock tank

5. '** 4*4 44. Fig. 4—25*1 * #HS Ml 411 4 <9-44 4#

$*ffl4 ##hb7} *!4-&4 4^44 *4. $* ##lt4 4s. 44 *

44 $4# 4144s* #44 *1* $4s *4.

# *44444 It]4* 7}^4 1** #44 #47} Figure 4-26 4 44 *4. 4**4 4** *44 44* $4s *$4 C7-10, Cn-i6, C17+* $44* **(heavy components) 4*4 *44 #*44 44 #4-1 s *4. 4# 37114 *4711- #4 EM4 A#it# *44 44 1

-194-

CONSTANT VOLUME DEPLETION

Figure 4-24. Constant volume depletion

20 —

0 —— Lab. data

0— CVD-2

1000 2000

Pressure (psia)3000

Figure 4-25. Relative liquid volume during constant volume depletion

-196-

Mol

e %

of e

ach c

ompo

nent

in pr

oduc

ed ga

s

100.00

10.00

■Q— N2

—A—

C7-10

C11-16

C17+

Pressure (psia)

Figure 4-26. Composition profile of each componentin produced gas during constant volume depletion

-197-

44 447} Fig. 4-274 44 5£4. 4494 4^44 *44# 4 4 444 «44# 4447} 444 54 444& 4 5 44.

### MM 4 4 4 459 4455 4# 44 4 4 (differentialliberation)4 44. 9# 44 A1 n-c- ##§# MM4 44 497}4 5 5.44444 4444 5-64 MM441 44 #HS MM494# 4:4 444 7}5» 54 444711 44. 444 4# #44444# 4 44 4444 M§4 9 #4 444 5# 44t)1 44. 444 4# 44444 5^ R? ##444 4444 44. 4 4444 4444 7}&# 444 44.

1. W 544 4#-8-4 442.. 447}^4 4455 #4 ^-sfl 7}^-544 44:3.. 51)4 #54 454. 7}54 ##71154 4#

4-4. It)b imifsf lean gas4 ## 49 (swelling test)

7W= 5# 7}^4#445 MJE9 0ftlr 7}5^4°)1 444 a?)]

4544. 7}54 944 44 ME4 #7#}# EH4 4#4 44 94 4 4#4 7H> #:# @i|$| ^4 444# 499 7}^5# 444 4

44 4# 9## 4^4444 -#4. 4 4544# 4)5444# 7>-s#99#9‘ lean gas# 454454 4 lean gas4 4## 447}5

94.68%, 4#7M= 5.27%, lSf7}i 0.05%5 4444 114-

711594# 7}54 B#m#44 #455 W#4 447)1 4 #31 4 #4 tWS: 44# 4444 1144 Hr«uE4 lean gas4 9944# 5447)1 44. Dt#MlE4 lean gas9 4449# fir ME 4 #9 #49 lean gas# #444 #J0c# 9444# 594# 444. 44 594#

-198-

Sepa

rato

r yie

ld (S

TB/M

MSC

F)160

0 1000 2000 3000 4000Pressure (psia)

Figure 4 27. Three-stage separator yield during constant volume depiction

-199-

##4## &4#^5 1 &4##& 4545 #4 4 #444 i?r*l§4 lean gas4# Stti 44 4 #4 4# 4 57}# 7l 4 5-4 M®# 2:714-^ s.4 ^5 ##5 4-0-# 5£ 44. Lean gas4 4 #44#44 45 4" 4c 7}3.tt MM4 lean gas4# 5#5# #4444 5### £444# #4#* # 5 44. 44 5##5 lean gas# S4 44 3E4444 4 ####44 7li#4#ji3. SE5 43L# #444 4*444-2-3. 444je. 44. lean gas## #44#44 5# #4 71144# #44 444- 4444 44 a.# 5# 4 #4#54 45 45. 44444 #4.

415# 7>^4 tir MEIS# lean gas4 5##4 4# 12.71,

30.46, 53.84, ^#31 65.38 5% 47}# #5^ 7}#44 414444. 4 # 44 4-0-4 jt?WE4 45-5-## 7}#:n. lean gas4# ##4#4 5# 4 444-#4 44444# #44#4# 432444 Fig. 4-284 44 44. zi#44 55 44 #4 97H #555. 4544 ItMH# #57}

##44 5444 11711 #555. 5#4 #55 4## 4## 4444 #44 447}jl 455 # 5 44. 445 45## #7}7} #4- #4#

#### 45445 44 4#4 45E5 #4=543= MM 454 4# 44# 54444 s.4 55#5 4444 54.

-200-

5000

3000 I ' I ' I 1 I '500 1000 1500 2000 2500SCF/BBL of dew point gas

Figure 4-28. Dew point pressures during swelling test

-201-

45# ^12.^5.5.0.44 4%

444#

-203-

I

!

i

45#(Seismic Waveform Inversion by Numerical Modeling)

41# (Seismic inversion theory)

1-1. Introduction

For the last ten years, seismic full waveform inversion has been

tackled and attacked by applied mathematicians and geophysicists. To

author's knowledge, the first attempt of full waveform inversion has

been done by Lines and Kelly (1983, personal communication). For the

computation of partial derivative seismograms with respect to the

coordinate of the wedge shape model, they used the numerical difference

scheme via finite-difference modeling technique. At that time, it was too

expensive research even to test the synthetic seismograms. Tarantola

(1984) claimed that the adjoint operator of wave equation makes one

compute the gradient vectors with respect to the velocity efficiently

without computing the partial derivative seismograms. Numerical result

using adjoint operator was given by Kolb et al.(1986) and Gauthier et

al.(1986). Mora(1987) extended it to the elastic problems with examples

of complex geologic structures. Unlike the way of inverting the

seismograms in the time domain, Pratt et al.(1990) applied the adjoint

operator in imaging the subsurface via the frequency domain

finite-difference modeling technique. Beside these gradient approach for

seismic inversion and imaging, Shin(1988) applied Gauss newton method

-205-

to invert the seismograms using the frequency domain finite-element

method. The possibility of using the Full newton method has been

investigated by Santosa et al.(1987). In this paper, the author(Shin)

review three different inverse algorithms such as gradient method,

Gauss Newton method and the full Newton method.

1-2. Finite-element and finite-difference formulation of wave equation

The discretized equation for the acoustic or the elastic wave

equation using the finite-difference method or the finite-element method

can be written (Marfurt, 1984) as

Mu{ t) +Ku( t) = F(t) (1)

where M is the mass matrix, K is the stiffness matrix and F(t) is the

source vector. If damping is included, the equation (1) is

. Mu( t) + Cu{ t) +Ku( t) = F{ t) (2)

where the C is the damping matrix.

Equation (2) can be solved in either the time domain or in the

frequency domain. The details on solving equation (2) in the time

domain can be found in many textbooks (Zienkiewicz, 1977; Bathe and

Wilson, 1986). However, recent inversion algorithm employed by

Tarantola (1984), Chavent (1986), Mora (1986) , Kolb et al. (1986), Shin

(1988) and Pratt et al. (1990) can be easily manipulated by Fourier

transforming equation (2). Taking Fourier transform of equation (2)

-206-

gives

Ku{ co) + ia)Cu{ <y)—aolMu(cd) =F( co) (3)

1-3. Inversion algorithm

For simplicity, one can write equation (3) into the form below

SU=F (4)

where the complex impedance matrix S is given by

S=K— mlM+ icoC. (5)

In the discretized domain, the complex impedance matrix S is given by the nn

by nn matrix, and U is 1 by nn column matrix , the F is the 1 by nn

column matrix and nn is the total number of nodal points in a given model

domain. Suppose that we can measure the wave field at the free surface or in

the borehole only. The residual error defined as the difference between the data

and the initial model response can be written as

£i=di~Ui, i=l,n (6)

where the subscript i indicates the receiver position on the surface or in the

borehole, dt is the measured data, and u -t is the initial model response.

Figure 5-1 shows the nodal connections being employed in next

discussions. The most common objective in optimization is to minimize the

-207-

Receivers on the surface

Figure 5-1. Conventions of nodal connection. N indicates the number of nodal points on the surface. NN indicates the total number of nodal points in the entire space. NE indicates the total number of elements in a given domain.

—208—

squared error ( /2 norm).

E(]>) = eT£= S <(dj—ui)T, (7)/= 1

where p denotes the vector notation for parameters such as velocity, density

and coordinates such that p=(p\,___,Pu), N and M indicate the number of

data points and the number of parameters respectively, and T is the Hermitian

transpose operator. It is important to note that complex impedance matrix S

and wavefield U are a function of parameter such as velocity, density, and the

digitized coordinates.

1-4. The Gradient method

The gradient method is a way of reducing the /2norm by updating the

parameter space iteratively

?lw>= T-SvE" (8)

where k is a iteration number. The partial derivatives of the objective function

represents the direction (positive or negative) for objective function to be

reduced. In other words, the objective function can always be reduced by

pursuing the direction of steepest descent Figure 5-2 illustrates the basic

concept of the gradient method. This is due to the fact that VE is orthogonal

to the contours of the constant objective functional value defined by 8E= 0 in

a parameter space, and +VE has the steepest ascent direction. The required

derivatives can be obtained either analytically or numerically using some

-209-

210

Figure 5-2. The dotted lines are contours on which E(P) is constant

The gradient direction is orthogonal to the contour direction at each point

finite-difference scheme. The numerical derivatives of the objective function for

each parameter are not usually employed for reasons of cost and accuracy. In

the following discussions, it is assumed that all derivatives of wavefield with

respect to parameters are computed form analytical expression by the

finite-element or finite-difference technique. The partial derivatives-of wavefield

U with respect to parameter (velocity, density, and the coordinates) can be

obtained by simple algebra. Let us define p as a parameter such as .velocity,

density and coordinates. Taking partial derivative of equation (7) with respect

to the parameter can be expressed in the matrix form

BE Bu\ 3u2 • 3um d\ — u{\dpi dpi dpi " BpiBE Bu\ 3u2 du^ d2 — U2dp2 Bp2 3p2 ' Bp2

= —2

BE Bu\ Bu2 Bun•

dPm) „ Bpm 3pm * ' ‘ Bpm, dtf— Ufj

(9)

Equation (8) can be written in matrix form

" P\k+\ ' Pi

k r bedpi

P2 P2BE

— — a

dp2

• •BE

. Pm. Pm. . dPM.

(10)

—211 —

where k is the iteration step and the step length a is yet to be determined.

The above iteration can be performed until one encounters the step which

does not reduce the objective function. In this case, the step length can be

adjusted and the procedure continued from the optimal point. There are several

techniques to adjust the step length a. For example, conjugate gradient method

can be applied to make convergence be faster than the simple gradient method.

The efficient calculation of the gradient vectors in seismic inverse

problems. In seismic inverse problems, the problem we have to consider in the

calculation of the gradient vectors is to compute the partial derivatives of the

wavefield with respect to the parameter P. In stead of the direct computation

of the partial derivatives, the gradient vectors can be efficiently calculated by

the adjoint operator "or the the transpose operator. Suppose that we are dealing

with a limited amount of surface or borehole data, the gradient vectors can be

expressed by-

r dE i r dui du2' dPi dpi dpy

= —2 •

dEdPm

dui du2dpm dpm

da,,dpi

du„dpm

d\ — %i

dn — un

(ID

where the- /(= 1,m) indicates number of parameters, and i= 1,, ,n

indicates surface or borehole data such that equation (11) can be written

v.

-212-

(12)

dui du„ dun+l du„n 1

dE 1 dpi dpi dpi dpi CLi-Ui

dpi ...

=—2 d„—un3E dUi dun dun+1 du„n

0

. dPm. dpm dpm dpm Hm 0 .

where n indicates the number of receiver points, and nn indicates the number

of total nodal points in the entire space. Let us consider the transpose of the

partial derivative wavefield with respect to one parameter. The partial

derivative of equation (4) with respect to p\ yields

9SS dpi dp\ u. (13)

Multiplying equation (13) by the inverse of S gives

<14)

The matrix notation of equation (14) is given by

r duidpi ' /l1'

• = 5_1

dunn dpi _ /r.

where the virtual source distribution /j is given by

(16)

/l1] UidS

. dp .

/l”” Wnn

making the transpose of equation (15) results in

dux du„„dpi dpi = [ A1- A””] 5-1 T

while similar procedures for /=2, •••, m results in

dui dUnn. dPi dPz .

dui dllnndPm dpm ,

= [ A1- hnn] S"lT

= [ fj- S"lT

(17)

(18)

(19)

Substituting equations (17), (18) and (19) into .equation (12) gives

dEdp i di~u{

dE

=—2 s~lT dn un 0

0 .dpm .

Equation (20) Can be rewritten as

dEdpi F A1 Vl

. ... %• = —2 ... ;

d£ *

dpM .[ Vnn\

(21)

-214-

where the back-propagated wavefield of residual on the surface or in the

borehole is defined as

di-Ui'V\ d-i—u-iv2 •

• = S~lT d„—unv„0

Vjin 0 .

The impedance matrix is equivalent to the time reversed impulse response

function (Green's function). Because of linearity and Hermitian conjugate of

Green's function, equation (22) can be written as

di-Uivi dz—u-iV2

• =s~l d„—unV„0

Vnn 0 .

(23)

Equation (23) means that when one propagates the time reversed residual, one

has the back-propagated wavefields to be correlated with the virtual sources

for the computation of the partial derivative wavefields in equation (20).

Talrantola(1985), Chavent(1986), Mora(1986) and Kolb et al.(1986) have exploited

this efficiency to solve the nonlinear least squares wave inversion by the

gradient method.

To test the adjoint operator of wave equation, the point diffractor model is

-215

taken for the computation of the gradient vectors. Figure 5-3 shows the point

diffractor model embedded in the homogeneous model. The velocity of point

difffactor is 1800 m/sec and the velocity of background velocity is 1600 m/sec.

Source point is located at the center of the surface and receivers are located on

the whole surface. Figure 5-4 shows the contour of gradient vectors with

respect to the velocity parameter of each grid point by back-propagating the

residual and multiplying the back-propagated wavefield with the virtual sources.

At the center of the model, the point diffractor has the biggest value except

the surface near the source point

1-5. The: Gauss Newton method

The second and simple approach for solving nonlinear least squares

problem is based on the fact that in the neighborhood of initial parameter, the

model response can expanded by Taylor series. Assume that the model

response can, be expanded in a Taylor series around initial parameter p: then, if

the data d\ is the measured data corresponding to the true parameter, we have

+

*5 (- -) + - -

-216-

0 1600(m)

&P

1600

*

(a)

*

(b)

Source

(c) (h)

(e)

*

(d) (i)

(f)

*

(g)

Figure 5-3. Point diffractor model embedded in homeogenous subsurface. Point diffractor is located at the center of the model. Source is located, at the center of the surface. Points which are perturbed are indicated by asterisks.

-217-

218

^NUAt*oiNie«eo!25vi*tn5i!i55oi?»56i*t86»t}p$6>$Si2$Stitduifc!'5i»«oS2»

%8*y%s&

Figure 5-4. Contour map of the gradient vector of the grid points shown in Figure 3. It is clear to note that gradient value is high at the center of the model.

dji-Un $p\du„dpi + dp2

dun dp2

■9 ( 8p\

+ dp„

1 f ^2 dZu„

du„dPm

) + • • o(d/>3)

(24)

where n denotes the number of receiver points, m denotes the number of

parameters, §P;= p‘i™~Pi , and o( ops) is the remaining term of Taylor

series expansion. Gauss Newton method consists of linearizing equation (24),

which gives

dux dux duxdx—ux dpi dp2 dpm

du2 du2 du2d2-u2 dpi dp2 dpm

du„ du„ dundn — u„ dpi dp2 dpm

#1

Sp‘2(25)

Because of mode and measurement errors, equation(25) can be solved by the .

standard least squares method, resulting in what are called the normal

equations

dux du„dpi dpi

du\ duxdpi dpm 8px dux du\

dpi dPm di—Ui

dux dun. dpm dpm

du„ du„ dpi dpm .

dun dun dPi dpm d„—u„

(26)

or

-219-

JTJ8p=JTe (27)

where J is the Jacobian of the parameter to model solution mapping. The

Gauss Newton method differs form the full Newton method which will be

discussed later, only by the inclusion of the second derivative term

Ri.i — ,m) in the full Newton method.

To improve the Gauss Newton method for large residuals and highly

nonlinear problems, two different alternatives were suggested. The first

alternative, called the damped Gauss Newton method, notes that the Gauss

Newton method always takes correct direction but with the bad step size for

highly nonlinear least squares only if / of equation (27) has full column rank.

Conte et al. (1981) calculated a correction factor A to give a more reliable

scheme.

Pk+I=pt-A UTJ)~lJTe (28)

The second alternative suggested by Levenberg(1966) and Marquardt(1963)

uses the model trust region approach. Consider the standard least squares

solution of equation (25) subject to the constraint such that

d\ — u{

d2 — u2

du\ du\ dpi dp2 du2 du2 dpi dp2

duidpmdu2dpm

zg zg- +A

dpi

8Pz

d„ — u„dun du„ dpi dp2

dun dPm . 8pm . 8Pm.

then one has

(29)

-220-

(30)(JTJ+Al)8p=fe.

Throughout this study, it will often be convenient to use equation (30) to

solve the seismic inverse problems. For highly nonlinear problems where Gauss

Newton step fails, the Levenberg-Marquardt and damped Gauss-Newton

methods are close to being in the steepest descent direction.

We will examine the role the partial derivative seismogram play in the

Gauss Newton method applied to point diffractor model. The physical concept

of virtual sources is critical to understanding both the Gauss Newton method

and the steepest method. In order to more fully understand the roles of the

partial derivative seismograms, we examine a model divided into many small

scatterers, as shown in Figure 5-3. It .gives insight to the behavior of the

seismic inversion using Gauss Newton method. Suppose that we divide our

model into an infinite number of infinitesimal sized cell, and wish to invert for

a point diffractor embedded in a homeogenous space! suppose the velocity of

homogeneous space is known. Figure 5-5(a) shows the residual between the

point diffractor seismogram and the initial guess. Because the true model

response has the direct arrival plus point diffractor hyperbola and the initial

response from homeogenous space has direct arrival only, the residual

seismogram looks like point diffractor hyperbola. Since the behavior of the

wave equation in time domain is somewhat easier to visualize than in the

frequency domain, it is instructive to examine the partial derivative seismogram

of the infinitesimal cell element in the time domain.

The undamped time domain finite-element or finite-difference wave

equation can be written

MU+KU=F (31)

-221-

E

0.790

1.550

I 900

1.790

« 9 000

« 9 550 £ 5.500

* 9 7505 3.000

" 3 POO

b

-W5

VsSYV^"^ "

C

TTTSB"nJy,Miwjr

Figure 5-5. The residual seismogram and the partial derivative seismograms with respect to the velocity of the points shown in Figure 5-3. (a) The residual seismogram between the point diffractor model response and the homogeneous model response (b) partial derivative seismogram with respect to the velocity of point (a) in Figure 5-3. (c) partial derivative seismogram with respect to the velocity of point (b) in Figure5-3. (d) partial derivative seismogram with respect to the velocity of point (c) in Figure 5-3. (e) partial derivative seismogram with respect to the velocity of point (d) in Figure 5-3.

223

Figure 5-5. continued, (f) partial derivative seismogram with respect to the velocity of point (e) in Figure 5-3. (g) partial derivative seismogram with respect to the velocity of point (f) in Figure 5-3. (h) partial derivative seismogram with respect to the velocity of point (g) in Figure 5-3. (i) partial derivative seismogram with respect to the velocity of point (h) in Figure 5-3. (j) partial derivative seismogram with respect to the velocity of point (i) in Figure 5-3. (k) partial derivative seismogram with respect to the velocity of point (j) in Figure 5-3.

Z

where M is the mass matrix , K is the stiffness matrix, and F is the source

vector, and dot indicates differentiation with respect to time. The partial

derivative seismogram of wave field with respect to the unknown parameter p,

becomes

M~^t +K-^f — f =-(-# U+~^U)dp dp dp dp (32)

•where f is a virtual source. Remembering that in the space domain, the virtual

sources have numerical support only where the parameter exists. In the time

domain, the virtual sources have numerical support only when the particle

displacement U and acceleration U are nonzero or only after generated

wavefield has arrived at the cell in question. In absence of other events, after

the direct waves pass through each cell, the virtual sources will return to zero.

Because of this, the partial derivative seismogram of the z’-th cell shown in

Figure 5-3 will show the hyperbolic pattern, as shown in Figure 5-5.

Using this insight of the partial derivative seismogram, we now return to

solving the normal equation (26). The first task in solving the normal equation

the normal equations consists of forming JTJ matrix and JTs vector where the

elements of J are the partial derivative seismogram. Form Figure 5-5, we can

see that in the high frequency limit, the partial derivative seismogram are

uncorrelated with each other. Thus, it is clear that JTJ matrix in the Gauss

Newton method will be diagonally dominant due to the misalignment occurring

in time and space, as shown in Figure 5-6. The JT£ vector appears in both

the Gauss Newton method and the steepest method.

Returning to point diffractor illustration, it is clear that residual seismogram

-224

000000000 0000 >10000 00000 >1000 000000 >1 00 1 0 ( 0 0 0 0 *1*1 09000000 >1 009000000 000 *9 *0000 0 0 0 * 190 0 0 0 000 00*0000 0 0 0 0 0 O UO 0 >100000090 •*•10000007 00 >1 000090 000300000 000030000 00000*000 00000 >1 000 000000009 000000000 000000000 000000000 ooooooooo 000000000 ooooooooo ooooooooo ooooooooo ooooooooo ooooooooo ooooooooo ooooooooo ooooooooo ooooooooo ooooooooo ooooooooo ooooooooo ooooooooo 00000000.0 ooooooooo ooooooooo ooooooooo ooooooooo ooooooooo ooooooooo ooooooooo ooooooooo ooooooooo ooooooooo ooooooooo

0 0 0 0 0 0 0 0 0 0 0 0

Figure 5-6. The normal equation appeared in solving Gauss Newton method for point diffractor model shown in Figure 5-3.

225

in Figure 5~5(a) will have a maximum correlation with the partial derivative

seismogram JT at the point diffractor cell number 25 in Figure 5-3. When the

residual seismogram in Figure 5-5(a) is superimposed on all possible diffraction

curve to form the normal equation applied to the Gauss Newton method in

inverting the point diffractor hyperbola (partial derivative seismogram) to form

JTe, the maximum correlation will occur on the point diffractor, as shown in

Figure 5-6. This operation is closely related to early migration work of

Hagedoom(1954) in his discussion of surface of maximum convexity. Thus,

Hagedoom's migration of prestacked data in a homogeneous velocity region has

element of both the Gauss Newton method with strong damping and the

steepest method. However, the situation becomes more complicated as the

geologic structure becomes increasingly complex and Hagedoom's technique

breaks down. However, it is generally true that when we consider wave events,

especially first arrivals activated form the virtual sources of each cell, the JTJ

matrix will be banded and diagonally dominant in form.

It is evident that the more we subdivide our model, the more partial

derivative seismogram with respect to velocity of point resembles the point

diffraction hyperbola. In addition, the partial derivative seismograms of each

velocity of point shown in Figure 5-5 will not strongly correlate with each

other such that JTJ matrix will be a diagonally dominant pseudo-banded

(negligible terms) matrix, although insurmountable difficulties arise when we

try to apply this approach to entire grid velocities.

1-6. The full Newton method

-226-

Unlike the Gauss Newton method, the full Newton method, which is robust

even for highly nonlinear problems, expands the objective function by Taylor

series in quadratic form (Bertsekas, 1982; Dennis et al., 1983)

E(p +8p) = E(p)+8pi

, Spi2 d2E(p)2 Jr8pi8p2 82E(P)

dp]8pt8pj 82E(P)2 ^

. (33)

Neglecting third order terms in equation (33), taking derivative of equation (33)

with respect to Sp yields

8E( i)+St>)d8pi + 8pi 82E(t) h 8p2~d2E(i>)

dpidp2 ^8ps 82E(i>)dpydp-i

8E( f}+8f>)dSp2

dE(b)8pt 8pi d2E(t>)

dp2dpi ~8p2 82E(8)#2

h Spz d2E(i>)dp2dp3

• =0

• =0

dE( P+Sp)d8pm

8E(t>)8pm 8pi d2E( t>)

dPmdPi h Sp2 d2E(i>)dpmdp2 + 8p„ 82E(b)

= 0

(34)

or in matrix notation

d2EU)') d2E(t) d2E(t) dE(b)dpi dpydpt dpipm dpi dpidE(p)

dPidPl dpi dp2dpm Spo dp2

d2E(ti) d2E(t>) d2E(t) dE(t)dpmdpi dpmdp2 dpi

.

dpm

-227-

We can explicitly express each element in equation (35) as

d2E(p) odpi z

d2Ui d2U2dpi dpi

d2u„dp2l

du\dp\

di~uid2—u2

d„—un

dundp\

+

du\dpi

du„dpi

Similarly, we can have the other elements

d2E(P)dp\dp2 =—2

and so on until

d2E(P)■%

= —2

d2U\ d2u2dp2dpi dp2dpi

d2U\ d2u2

dp2m dp2m

dp2dpi

duxdpi

dlUr

di~uid2 — u2

dn Un

du„ ‘dPi .

di~Uid2 — u2

d„ — u„

+

du\dp2

du„dp2

+

228-

dui du„dpm dpn

duidPm

du„dPm

(37)

Defining A to be the Hessian matrix with element » equation (35) can

be rewritten as where

(JTJ+ R)8p——JTe or A 8p=B (38)

where

f=

dux du\ dux •dpi dp-i . dpm r spx r dx—u{\du2 du2 du2dpi dpm Sp2 d2—u2

, 8p= , £ = •

8un dun dun • •dpi dp2 dpm. [ SPm\ d„ — u„

*/./— 2d2Ui d2u2 d2u„

dpfipj dpjdpj dpjdpj

d\—ux d2 — u2

d„ — u„

jJ+R—A, and B=—Jte.

The minimum can be reached by updating the current parameter p by the

amount of 8p. Thus we are lead to the general iterative scheme

pk+l = pk+A~lB; (39)

This progress can reach toward the global minimum by full Newton method,

-229-

which is only ensured if A is positive definite. The details on necessary and

sufficient condition for an extreme value can be found in Beveridge et al.

(1980).

Full Newton method can be a fast local method to the nonlinear least

squares problem since it has locally quadratic convergence rate when the

quadratic assumption is valid. However, the step length given by equation (39)

will not necessarily give the minimum along the direction if the objective

function is far from the quadratic model. We therefore deduce that while full

Newton method can be very efficient in the neighborhood of the minimum,

away from this point, it may be no better than other simpler method.

1-7. Efficient calculation of Hessian matrix

As we used the adjoint operator or transpose operator in calculating the

gradient vector, we can efficiently calculate the remaining term R;j in equation

(36) using adjoint operator of wave equation. Supposing that the partial

derivatives used in Gauss Newton method are computed in the entire space

(not just on the surface), we can use the transpose operator in calculating the

remaining term Rfj in equation (36).

Define Rij of equation (36) as

d\—U\d2 — u2

d» — un

i=l,m, ■ j=l, ,.,m. (40)

where m is the number of parameters. Recalling computation of the partial

Ru=- 2d2Ui d2u2 9%

dpjdpj dpidpj dpfipj

-230-

derivative wavefield, we have

s- dudpi

9Sdpi u, (41)

where S= K+icoC—a)2M. Taking the second derivative of equation (41) with

respect to Pu gives

o d2u . dS du _ _ dS du _ d2Sdpjdpi dpj dpi dpi dpj dpjdpi U'

For convenience, let's define a new virtual source term

/** dS du , dS du , d2Sdpj dpj dpi dpj dpjdpj * (43)

Then equation (42) leads to

c—dhe_ =_f* (44)

As we did in calculation of gradient vector using transpose operator,

multiplying equation (44) by the inverse of the 5 matrix and taking the

transpose gives

(-^)r~(/rcs-v. (45)

Like we employed in calculating gradient vectors, adding and substituting

equation (45) into equation (40) gives

-231-

Ru = -2 (f*)T(S~1)T

d\ U\

dn—un0

0

i=l, ‘",m, j=l,—,m. (46)

Thus, we can efficiently calculate the remaining term R;j in the Hessian

matrix of equation (36) by computing the partial derivative" wavefield in the

entire space, forwarding modeling with complex conjugate residual as a source

function and multiplying this back-propagated wave field with the virtual

sources given by equation (43). Figure 5-7 show the matrix equation of

equation (35) when we applied Full Newton method to solve point diffractor

example using full Newton method. It is not clear to see the big difference

between the normal equation shown in Figure 5-6 and the Hessian matrix

shown in Figure 5-7. Thus, in highly nonlinear seismic inversion, full Newton

method does not help inversion process go faster that Gauss Newton method.

-232-

0 0 0 0 0 9 000000000000000000 0 0 0 O 0 o 0 0 0 0 0 0 0

000000000 0 0 0 0 0 0 0

0 0 0 0 0 6 0 0 0 0 0 0 0 6

0 0 0 0 0 0 0 0 0 0 0 0 0 6

0000000000000000

0 -10 0 0 0 0 0 0 0 0 0 0 0 00000000

0 0 0 -10 0 6 0 0 0 0 -|0 0

0 0 0 0 0 0 0 0 0 * 0 0 0 0

000000000000*000

0 00 0 0 9 0 0 00 0 0 0 0 -100 0 * 0 0 1 0

• 0 0 0 0 0 0 I130 0 0 0 0 0 0 0 0 0 0 0 00 0 7 0 0 0 00 0 0 H 1 0 o0 0 0 1 470 0 0 0 0 0 0 30 0

0 0 0 0 0 -1

0 0 0 0 0 0 0 00000 16 000

0 0 0 0 0 0 0 0 0 0 0 0 0 0 03000000

0 0 0 0 0 0 0 0 0 0 0 0 0 0 00000100

0 0 0 0 O 0 < 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Figure 5-7. The normal equation appeared in solving full Newton method for point diffractor model shown in Figure 5-3.

-233-

/

(Pseudo waveform inversion of reflection seismograms in the frequency domain)

2-1. Introduction

In the seismic reflection method, the velocity distribution of the

subsurface plays an important role in seismic data processing and

interpretation, such as in the processes of stacking, migration and depth

conversion. Interval velocity is commonly calculated by applying the Dix

formula to a prior measured rms velocity and traveltime at zero-offset.

Since, for the most part, a realistic geologic model does not satisfy

Dix's assumption, it is necessary to estimate the velocity model by an

inversion method.

According to the technique used to solve the Green's function of the

wave equation, seismic inversion may be classified into two groups. One

approach is waveform inversion based on the wave equation. Linearized

waveform, inversion is based on the Bom approximation, where weak

scattering is valid (Tarantola, 1984a," Bleistein et al, 1985).

Non-linearized waveform inversion makes use of the full information of

the observed data, and this inverse problem can be solved using an

iterative method, where the gradient is usually computed by using the

adjoint equation (Tarantola, 1984b, 1986," Kolb et al, 1986; Mora, 1987;

Shin, 1988; Pratt and Worthington, 1990; Zhou et al, 1993). This

waveform inversion can estimate the low frequency information of the

velocity distribution, but it en ounters great difficulty when applied to

-234-

real seismic data, as seen in source wavelet estimation, non-linear

behavior of wave propagation, intensive computation in solving the wave

equation by numerical methods, and so on.

Another approach is traveltime inversion by a ray tracing algorithm.

In this kinematic inversion, the velocity model can be estimated by

using an optimization technique which minimizes the difference between

the traveltime calculated by forward modeling and the traveltime picked

in the recorded data (Gjoystdal and Ursin, 1981; Bishop et al, 1985;

Keydar et al, 1989; Lines et al, 1993). Unlike traveltime inversion,

coherency inversion maximizes some measure of coherency computed for

unstacked trace gathers in a time- window along traveltime curves

generated by ray tracing through the model (Landa et al, 1988; Reshef

et al, 1991). But traveltime and coherency inversion may lead to a

failure of the inverse procedure when the initial model is far from the

true model.

In this study, we introduce a pseudo waveform inversion in order to

estimate the velocity model from prestack data. The following trick is

used to imitate the waveform inversion: the high frequency limit

seismogram computed by ray tracing is low-pass filtered and then used

to match the residual and perturbation seismogram when the initial

model is more or less far from the true model. Even though the initial

model is not in the neighborhood of the true model, the low frequency

band information can be employed to improve the reliability and

effectiveness of the inversion. The following technique is applied in

order to make improvements on the stability of the inversion process;

-235-

(1) layer after layer determination of inversion parameters,

(2) an alternative choice of velocity and interface coordinate parameter,

(3) frequency band increasing technique.

We will describe the proposed method and demonstrate its application to

synthetic and field data.

2-2. Theory

2-2-1. Damped least square method

In seismic exploration, the wavefield recorded at the surface or in a.

borehole is, a function of the source-receiver position and the material

parameters of the subsurface, such as velocity, density, attenuation

factor, and so on. The purpose of inversion is to quantitatively

determine, the model parameter, velocity distribution, from the observed

data. Since the model parameter is essentially non-linear with its model

response, we can use iteratively an optimization technique that

minimizes the sum of squares of the differences between the observed

data and the calculated data. In the damped least square method, the

solution vector is given as (Lines and Treitel, 1984),

dp = (JTJ + PlT'fg (1)

where <5p is the parameter correction vector, J the Jacobian matrix, JT

the transpose of J, g the error vector, and I the identity matrix of the

order of JTJ, and 0 is the damping factor. The parameter correction

vector dp should be real-valued, but the Jacobian matrix J and the

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error vector g are complex-valued in the frequency domain. The real-valued dp can be obtained as follows; Let J= a+bi and g = c+di.

Augment the matrix J by its complex conjugate, similarly augment g. We thus obtain the new matrices J and g given as following

(2)

The complex conjugate represents the negative frequency terms.Consequently, JTJ and JTg are always real-valued so that we can

always obtain the parameter correction vectors as the real values.

j'Tj' = 2(a2 + b2) , j'V = 2{ac + bd) (3)

2-2-2. Parameterization and Forward calculation The velocity model (Fig. 5-8) is 2-dimensional with horizontal

distance x and with depth z measured downward. The surface, given by the line z= 0, is called interface n = 1. Below the surface there are N— 1 interface which are labeled n=2,3,“\N. Each subsurface

interface is continuous and is made up of straight line segments. A node-point of an interface is a point where two straight line segments connect. Let (Xmn,Zmn) be the coordinate of the tfzth nodepoint of interface n. The model contains N layers designated by Layer n

(>z = 2,3,***, AO, which is the layer that lies between interface n (on top) and interface n+1 (on bottom), has constant interval velocity v„.

The model is characterized by the model parameter vector

-237-

238

>x

Source ReceiverSurface

Interface = 1

Interface = 2

Interface = 3

Layer = 1 velocity =Vr

Layer = 2 velocity = V.

Interface nodepointInterface =n

Interface —n+1

Layer -itvelocity =V

Interface =m

Interface =N-1

Interface =NLayer =N-1 velocity =VAr.i

Layer=N velocity =VN

Figure 5-8. An TV-layer model. v„ is the interval velocity in the w-th

layer, and rn the traveling distance of the ray. C( xn,zn) is the

coordinate that the ray passes through %-th interface.

(4)

This method makes up of ray tracing. The shooting ray technique

considers a ray that travels from the source to the reflecting point A

and then back to the receiver. As shown in Figure 5-8, let the position

of the source and the position of the receiver be fixed. Each raypath

considered will start at the source and end at the receiver. We will

consider a raypath for a primary reflection from each subsurface

interface m, where m=2,3,•••,N.

The ray that reflects from point A on subsurface interface m is

shown at the raypath from the source to A to the receiver. The

traveltime is the sum of the traveltime from the source to A and the

traveltime from A to the receiver. By the principle of reciprocity, the

traveltime from A to the receiver is the same as the traveltime from

the receiver to A. Thus we need to treat only the downgoing path of

source to A, as the other path will have a similar treatment. For the

given downgoing path, let the ray pass through interface n at point

C— (xn _ z„). The travel distance in the %-th layer is

r„ = V (**+i -xn)2 + (zn+1-z„)2 (5)

and the traveltime in the %-th layer is rjvn. Thus the traveltime for

the primary reflection from interface m is

P — [ y«. X mn, Zmn]

-239-

(6)

In this analysis we consider only traveltimes and do not take into

account amplitudes of reflections. Thus for an impulsive source, the

primary reflection from interface m can be represented by the wavefield

um{i) = 8(t—Tm). (7)

The primary reflections from all the N interfaces can thus be

represented by

NNu(t)= 2 Um(t)= 2 8(t—Tm).

m=2 m=2(8)

The Fourier transform of this equation gives

(9)

This wavefield can be low-pass filtered to yield more realistic input

data.

2-2-3. Calculation of Analytical Derivatives

The derivatives with respect to the model parameter vector or

Jacobians are essential in an iterative damped least square method. The

numerical partial derivatives are not usually employed for reasons of

cost and accuracy. Jacobians are analytically, computed for the efficiency

—240—

of the inversion procedure. The partial derivatives of U(co) with respect

to the interval velocity vt becomes

= -fj'-«®> 00)

The partial derivatives of U{(o) with respect to the crossing interface

point C( xn , z„) become

dUico) _ • / xn xn-\dxn Vrt—1 xn—1

and

9U(cd) . f Zn %n—l—=---- . = 1(0 (----------------OZn Vn—\ Yn—1

%n+l%n

■^n+1 xnVn r„ ) • U(co).

(11)

(12)

For the derivatives of the model parameters, let us consider the

interfaces that consist of two straight segments AB and BC, which

have three nodepoints A(Xi,Z,), B(Xm,Zm), and C(Xr,Zr) (Fig. 5-9).

The coordinates that the ray cuts the interface AB and BC are given

by Dix/Z,) and E(xj, zj). Perturbing the nodepoint B to an arbitrary

position S’, the derivatives with respect to the model parameter B

have something to do with the derivatives of the crossing points D and

E. Applying the chain rule toau

ax. andau

az„

dU = dU dx,- 8Xm dx; 8Xm (13)

-241-

242

Perturbed interfaceRaypath

Raypath

Interface Interface node point

Figure 5-9. Geometry used to compute partial derivatives with respect to coordinate B( Xm,Zm).

3U du dZiaz« dZi az^

dU du dXj

i

dXj a%«

du du dzjaz„ dzj az,

The mathematical notation of the above relationship are given by (Shin,1988) !

X{ =Xm-Xi Xm—Xi \

__7 y __7 &n.m(14)

Zi = ■^‘-Z'-x,+Z.Xm—X/

Zm-Z, X,

•n / i y __ m7—7 zj'^m 7 _7

_Zm_Zr_ ,y Zm Zr

Taking the partial derivatives of equation (14) with respect to Xm and

Zm leads to

_ i _ | -±l>»___l |axm 1 1 Zm—Zt 1 (15)

=i_| . • |

243-

From equation (15) and equation (13) we have

ML - + w*i%

ML % dudzi + Wu dU

dZj

(16)

where WXi = . W* ~ SXm w« = -&:•mi w“ = -§£

The weighting function has maximum value 1 at the nodepoint B

and minimum value 0 at the nodepoint A or C. The amplitude and the

phase of the forward wavefield is constant. However, the amplitude and

the phase of the partial derivatives that are analytically computed in the

frequency domain are function of frequency, the medium velocity, and

the ray trajectories.

2-2-4. The frequency band characteristic of the objective function

In the damped least square method, we minimize the residual. The

residual is defined as the sum of squares of the differences between the

observed data and the calculated data. The objective function can be

given by

5f(p) = (d - u)T(d - u) (17)

where d is the observed data, u the calculated data, and T denotes the

transpose.

For the purpose of illustrating the frequency band characteristic of

the objective function, we compute equation (16) for the syncline model

shown in Figure 5-10. The syncline axis Hi.0,0.8) is moved to each

grid point and the size of grid cell is 25m. In this case the parameters

of the objective function are the x-and 2-coordinate of the syncline

axis. Figures 5-ll(a), (b), (c) and (d) show contours of the objective

function when the frequency bands are at 5, 10, 15 and 20Hz,

respectively. From Figure 5-ll(a), it can be noted that the objective

function has a global minimum at the correct position of the syncline

axis and has a very smooth contour line. As we high-pass filter the

data, the local minimum begins to appear as shown in Figures 5-11(b),

(c) and (d). Even though the initial value is not adjacent to the true

model, inversion in the low frequency band can be stable and robust

because of the larger convergence radius. We infer that the convergence

rate in the low frequency band is faster than in the high frequency

band. But the resolution in the low frequency band is low. Therefore,

the lower frequency band is used in the first stage of the minimum

search. The model parameters computed from this stage are used as

initial guess for the next high frequency band.

Then, let us examine the objective function of traveltime inversion.

Figure 5-12 shows the contour of the objective function obtained by the

-245-

DEP

TH

(km

)

DISTANCE(km) 2.00.0

velocity = 1500 m/s

velocity = 2500 m/s

Figure 5-10. 21X21 grids used to compute the objective function with respect to geometric coordinate P(1.0,0.8), where the node point P is moved to each grid point The size of grid cell is 25mX25m.

Ver

tical

Pert

urba

tion

(m)

Ver

tical

Per

turb

atio

n (m

)

>250 -200 -150 -100 -50 0 50 100 150 200 250

Horizontal Perturbation (m)

-200 v

i i t i•250 -200 -150 -100 -50 0 50 100 150


(a) (b)

-250 -200 -150 -100 -50 0 50 100 150 200 250


100 -

-200 -

-250 -200 -150 -100 -50 50 100 150 200 250


(c) (d)

Figure 5-11. The objective function of the pseudo waveform inversion with respect to geometric coordinate. The frequency band of (a), (b), (c) and (d) is 5, 10, 15. and 20Hz, respectively.

-247-

Ver

tical

Per

turb

atio

n (m

)

Figure 5-12. The objective function of classical traveltime inversion with respect to geometric coordinate of the model shown in Figure 5-10. (a) Large scale, (b) Small scale.

classical traveltime inversion. We can not find the correct position of the \

global minimum in Figure 5-12. If the initial value is set up at position

A shown in Figure 5-12(a), the inversion algorithm may not converge

to a global minimum but diverge to a local minimum. When the

objective function is magnified in the vicinity of the minimum on a

small scale (Fig. 5-12(b)), we realize that the model parameter with

respect to the horizontal perturbation is very insensitive (it is parallel to

the horizontal perturbation axis) and does not search for the solution.

2-3. Examples

2-3-1. Synthetic data

To examine the efficiency of inversion algorithm, it is necessary for

us to apply the proposed method to the synthetic data where the true

model is known. We generate the synthetic seismograms by the time

domain finite-difference modeling using the 2-D acoustic wave equation

for the model shown in Figure 5-13. Table 5-1 show the acquisition

parameters for the synthetic seismograms.

Because of assumption that the amplitude and the phase of the

recorded wavefields are constant, we can replace the recorded data by

the pseudo waveform data by the procedure shown in Figure 5-14.

Figure 5-15(a) shows the synthetic data in common shot gather where

the source is located at 5km. Figure 5-15(b) shows CMP gather data at

400-th CMP number. Figure 5-15(c) contains the velocity spectrum

-249-

DEP

TH (k

m)

DISTANCE (km) 16.00.0

1800 m/s 1500 m/s

2000 m/s

2500 m/s

3000 m/s3500 m/s

4500 m/s

6000 m/s

Figure 5-13. A model for the time domain finite difference modeling where the shot is located at surface. The number of shot is 206, that of receiver is 101, shot interval is 60m, and receiver interval is 30m.

Hyperbolic Equation

Low Pass Filtering

Fourier Transform

Primary reflection ofP-wave

Sorting

Semblance Function

Traveltime Construction

Common Shot Gather Data

Velocity Spectrum

Zero-offset time and Velocity Picking

Pseudo Waveform Data

Common Shot Gather Data

CMP Gather Data

Figure 5-14. The flowchart of replacement into the pseudo waveform data.

-251-

0.00DISTANCE (km)

Figure 5-15. Synthetic seismograms for the model shown in Figure 5-13 and velocity sepctrum. (a) Synthetic seismogram generated by the time domain finite difference modeling. Source is located at 5km.

-252-

0.00

DISTANCE (km)3.00

0.000

0.900

1.000

2.000

2.500

3.000

3.900

4.000

4.500

5.000

Figure 5-15. continued, (b) CMP gather data at 400th CMP number. The trace interval is 120m and the CMP fold 25.

—253 —

TIM

E (s

)

VELOCITY (km/s)

Figure 5-15. continued, (c) Velocity spectrum of Figure 5-15(b). The symbols O denote the zero-offset time and velocity which is picked from the primary reflection events.

-254-

0.00DISTANCE (km)

3.00

0.000

0.500

1.500

2.000

2.600

3.000

3.600

4.000

6.000

Figure 5-15. continued. (d) Synthetic seismogram reconstructed by hyperbolic equation using the zero-offset time and velocity picked in the velocity spectrum shown in Figure 5-15(c).

-255-

Table 5-1. Acquisition parameters of the finite-difference modeling.

Number of Shot 206

Shot Interval 60 m

Offset 200 m

Number of Receiver 101

Receiver Interval 30 m

Record Length 5 s

Sampling Interval 0.875 ms

Source WaveletGauss first derivative wavelet (major frequency : 25 Hz)

Grid Distance(dx, dz) 7.5 m

Boundary Condition sponge B.C.

where the symbol O denotes the picked velocity and zero-offset time.

Figure 5~15(d) show the synthetic seismograms that are generated by

convolution of the picked reflection traveltimes with a 25Hz Gaussian

first derivative wavelet. Comparison between Figures 5—15(b) and (d)

indicates that the picking procedure is reasonable. There are many

advantage in the traveltime picking in the velocity spectrum • (1) Since

it is closely related with interpretative work, the inversion procedure

directly interacts with interpretation and data processing. (2) A priori

information, the picked zero-offset traveltime and velocity, can be

utilized for the setup of the initial model. (3) We can easily pick the

-256-

zero-offset time because its profile is similar to time stack section.

After we replace the 151 set of CMP data, every 5th CMP number

from 90th to 840th CMP number, into the input data for the inversion

through the above mentioned procedures, we invert the seismic data as

follows : (1) The inversion frequency band is from 2Hz to 10 Hz with

2Hz frequency increment. (2) The iteration per each frequency band is

10. (3) The determination of inversion parameters is progressively layer

after layer so that the inversion algorithm may avoid convergence to a

local minimum(Kolb, 1986). (4) The inversion parameters are the velocity

and the interface nodepoints of the 3rd, 4th, 5th, 6th and 7th layer.

First, we consider velocity inversion when the interfaces of the

model are fixed. Figure 5-16(a), (b) and (c) are the true model, the

initial model and the final model, respectively. Although the initial values

are away from the true model (maximum' velocity deviation is 500m/s.),

the final model is very closely with the true model ; Maximum velocity

error is within two percent(50m/s) at the deepest layer. The estimated

velocity of upper layer has good resolution within a few m/s error.

Second, we study interface inversion when the velocities are fixed.

Figures 5-17(a), (b) and (c) are the true model, the initial model and

the final model, respectively. Even though the initial model is a dipping

layer model (the initial values are far 200-300m from the true model),

the interfaces of the final model are nearly at the correct position:

Maximum error happens at the 7th layer within one percent (20m).

When the dip angle of the interface is less than 45 degree, the z

-coordinate of interface nodepoints is more sensitive than the x

-257-

DISTANCE(km) 160 (km/s)

(b>

DISTANCE(km) (km/s)

(c)Figure 5-16. Result of velocity inversion when the interface is fixed, (a) The true model, (b) The initial model, (c) The final model.

-258-

r'',»: ■J

16.0 (km/s) 1.5

I6.0

(a)

(b)

DISTANCE(km) q (km/s)

(c) .Figure 5-17. Result of interface inversion when the velocity of the model is fixed, (a) The true model, (b) The initial model, (c) The final model.

-259-

-coordinate. If the ^-coordinate of the interface nodepoint is perturbed

in the horizontal layer, the change of traveltimes can not occur.

Finally, we examine simultaneous interface and velocity inversion.

When there are excessively changes in the magnitudes of the parameter

correction components, inversion can be unstable. In order to overcome

this problem, we introduce an alternative choice of velocity and

geometric coordinate parameter. The velocity inversion is performed at

the first iteration, the interface inversion at the next iteration step, and

simultaneously the velocity-interface inversion at the third iteration.

Thirty iterations are run in each frequency band. Figures 5-18(a), (b)

and (c) contain the true model, the initial model and the final model,

respectively. The results of inversion are in agreement with the true

model. But this inversion result gives lower resolution than the above

inversion result when the interface or the velocity is fixed. Especially,

the result of the deepest layer is not so good ; Maximum interface and

velocity error is about 50m and lOOm/s, respectively. In the progressive

downward determination technique, the velocity distribution of the

shallowest layer should be accurately estimated if we want that of the

deepest layer to be evaluated.

2-3-2. Field data

We apply the pseudo waveform inversion to the prestack field data

of Korea offshore. Before picking the reflection traveltimes, we carry out

some preprocessing (such as CMP sorting, gain, muting, velocity

filtering, arid so on) so that it may be preferable to identify reflection

—260 —

DISTANCE(km) 16.0 (km/s) 1.5

I 6.0

(b)

DISTANCE(km) jgg (km/s)

(c)Figure 5-18. Result of simultaneous velocity-interface inversion, (a) The true model, (b) The initial model, (c) The final model.

-261-

events in the velocity spectrum. Figure 5-19 shows the migrated stack

section by the conventional processing, where the prestack data is

inverted with respect to the horizon A, B and C. The initial model is

shown in Figure 5-20(a). The initial guess of the horizon B, it is set

up as the horizontal layer, is chosen to be significantly different from

the time section so that we may demonstrate the stability and feasibility

of our inversion algorithm. .The initial guess of the horizon C is set up

as the dipping layer. The starting velocity of each layer for the

inversion averages the velocity which is obtained by applying the Dix

equation to the picked velocity and t0. The depth of sea bottom

obtained from bathymetric data (about 80m) is fixed during inversion.

We adopt an alternative parameter choice and the progressive downward

determination technique in order to make an attempt of the stable

inversion. The final model shown in Figure 5-20(b) is similar to the

time section, but the layer thickness of the horizon A is thin and that

of the horizon C is thicken.

We compare the estimated velocity with the sonic log velocity which

is directly converted from transit time, where the nearest well from the

seismic line is far away at about 35km (Fig. 5-21). The solid line

shown in Figure 5-21 depicts the sonic log velocity from 1200m to

1800m and the dashed line shows the inverted velocity corresponding to

the horizon B and C. It is difficult to compare the depth of the horizon

B and C with the well logging data because geologic events appear

between the seismic line and the well, such as faults, erosion, intrusion,

-262-

TIM

E (s

)

DISTANCE (km)

Water bottom

Horizon A

Horizon B

Horizon C

Figure 5-19. The migrated section. The profile of the picked zero-offset time is similar to this section.

DISTANCE(km) 9.0 (km/s)

(a)

I

I

1.5

5.0

DISTANCE(km) 90 (km/s)

(b)

Figure 5-20. Result of simultaneous velocity-interface inversion with the prestack field data, (a) The initial model, (b) The final model.

—264—

VELOCITY (m/s)2000 2500 3000 3500 4000

J L i L

1300 -

Horizon C1400 -

Horizon Bf—i 1500 -

1600 -

1700 -

1800 -1

Figure 5-21. Comparison between the sonic log velocity (solid line) and the estimated velocity (dashed line). The sonic log velocity is directly converted from transit time.

-265-

and so on. The estimated velocity of the horizon B is in accord with

the sonic log velocity trend to about 1450m. That of the horizon C is

to some extent higher than the velocity distribution of sonic log below

1450m, because the depth of the horizon C is more deep (the seismic

line is located toward the middle part of basin).

2-4. Discussion

In order to estimate the velocity model of subsurface, we develop

the pseudo, waveform inversion. We can obtain following conclusions

from our experiments.

(1) Since- the perturbation seismograms can be computed analytically by

using- ray tracing, the proposed method can much reduce

computational cost.

(2) In order to improve the reliability, effectiveness and stability of the

inversion, the low frequency band information can be employed so

that the- pseudo waveform inversion can be more stable and robust

than traveltime inversion.

(3) Even though the traveltime picking which is based on hyperbolic

approximation may include an error to some extent, especially in

steep dip with low signal-to-noise ratio, our inversion has been

successfully applied to the synthetic and the prestack field data. But,

it is necessary to improve the accuracy of traveltime picking because

a# Si

—266 —

the traveltime picking plays an important roles in our inversion

algorithm.

(4) The proposed algorithm can be easily applied to the time-to-depth

conversion in the stacked data by normal -incidence ray tracing as

well as to the traveltime tomography.

-267-

(Inversion of seismic refraction data in the frequency domain using ray tracing)

3-1. Introduction

Seismic refraction technique has been widely used to delineate the

shallow subsurface. The refraction data inversion has been used for a

long time to calculate the field static correction (Russell, 1989) and to

estimate the velocity-depth model (Landa, 1994).

The seismic traveltime inversion can be classified into two methods,

depending upon the choice of objective function. One is to minimize the

error (or difference) between the field data and the synthetic

seismograms. The other is to maximize the cross-correlation between

the synthetics and the observations (Sen, 1991). Waveform inversion

methods can be broadly classified into the direct inversion methods

(Clarke, 1984; Yagle and Levy, 1985) and the iterative inversion schemes

or nonlinear least-square inversion methods (Tarantora, 1987; Pan et al.,

1988).

In order to accomplish seismic inversion, ray tracing is one of

important tools to calculate the forward seismogram or the traveltime. In

shooting ray methods of ray tracing (Cassel, 1982; Cerveny, 1977), a fan

of rays is shot from a source point to the general direction of the

medium of interest. The correction path and travel time to connect the

two points may then be approached with successively more accurate

—268—

guesses or by adjusting the shooting angle carefully. When structure is

complex or receiver is arranged in a line, the shooting ray method is

very effective for the calculation of traveltime. Bending ray methods

(Um & Thurber, 1987; Perey, 1988) start with an initial, probably

incorrect guess for the incorrect raypath. The raypath is bent by a

perturbation method until it satisfies a minimum traveltime criterion.

When structures can be represented into analytic function to define

velocity function, it has the merit of being simple and efficient. There is

a method using finite-difference method (Vidale, 1988), but it is difficult

to express the traveltime in function of velocity and coordinates of the

medium.

In this paper, it assumes that every geologic models can be

subdivided into polygon regions having constant velocity, we designed

the shooting raytracing algorithm which is applicable to the complex

structural problem, and analytic calculation of the partial derivative

seismogram. Inversion process is based on the fact that first break of

head waves can be simplified into the delta function having unit

amplitude and with phase shift due to traveltime. In this way, we can

exploit the advantage of the full waveform and the travel time inversion.

We used the damped least-squares method (which is the Gauss Newton

method) for the inversion of head wave data. We applied this inversion

method to the synthetic and real seismogram.

3-2. Theory

-269-

To calculate the travel time of head wave, we assume that

subsurface can be divided into the blocky regions consisting of straight

line segments and head waves travel along the interface, as shown in

Figure 5-22. Unlike the conventional shooting ray tracing of fan of rays

from the source, a series of rays along the interface at infinitesimal

distance interval are shooted according to the Snell's law. After

collecting the rays which is nearest the source and the receivers,

calculating the travel time corresponding to the those ray paths and

adding the. travel time along the interface gives travel time as below

where, L is the number of ray path segments, y(r^) is the velocity of

medium, rk is the distance defined as

rk = f,{xk+l-x^)2 + (zk+r~Zk)2 , k= 1,2,3,—,/

where, k- is the number of ray path segments, and %k and are the

coordinates of the points on the interfaces which ray passes through.

Since we are dealing with the first break in seismic refraction

prospecting; we discarded the wave events following the first breaks

and assume, that seismogram can be a unit delta function without any

reflection, diffraction and other waves. Picked seismogram can be given

as

u(t) = 6( t - T ) (2)

-270-

source receiver

Figure 5-22. Ray path in a layer with irregular interface. Interface is assumed to consist of straight line segments and velocity in each layer is constant.

where T is the travel time of headwave. Because the travel time of

head wave is a function of the velocity and the coordinates of the

interface, we can take the partial derivatives of Equation (1) with

respect to velocity and coordinates. The key point to note is that we

will take Fourier transform of Equation (2) in the frequency domain.

Fourier transforming Equation(2) gives

U(o>) = eiwT. (3)

Figure 5-23(b) is a synthetic head wave seismogram, convolved

with Gaussian first derivative wavelet having 50 Hz major frequency

whereas Figure 5-23(a) shoes the ray paths computed by shooting ray

tracing.

In frequency domain, the partial derivative with respect to the

velocity of n-l'th layer is calculated by single algebra

~dv'Q)^ =------+ U(co). (4)ut'n—1 y„-i

The partial derivatives with respect to interface coordinates can be

given as

dU(a>) _ • / xi xi-1dx; \ v(ri-x) r,-i

Xj+1 ~ Xj

v (rt) Yi (5)

-272-

DISTANCE (m)

lOOOm/sec

2000m/sec

3000m/sec

Figure 5-23.(a) Raypaths in a 3-layer model with curved interfaces, (b)

Synthetic seismogram computed by ray tracing. Source is Gaussian first

derivative wavelet having 50Hz major frequency.

-273-

dU(co)dz{

wf) «•> (6)%j—iv(r{-1) n-i

where, / (i = 1,2,3, •••, /+1) is the number of segments. From

Equation (4), (5) and (6), we can see that the phase and the amplitude

of partial derivative seismogram in frequency domain change according

to the frequency, velocity and coordinates of raypath. Since we assumed

that interface of the blocky region constrains to move together as a

straight line when the point of blocky layer is perturbed, we followed

the procedure developed by Shin(1988) to multiply weighting coefficients

to Equation (5) and (6). The partial derivative seismograms passing

though the interface sharing the movable point (xm, zm) can be given as

- -dx (7)

it - +1,^ (8)

where, Wx. = , % = , Wxi = 3£l , Ffc =dZj dzm *

The pseudo full waveform inversion named in this study is the

nonlinear inversion method utilizing the good advantage of full

waveform inversion in the frequency domain.

Data of observations are expressed by the vector

-274-

D— (Di, D2, D$'Dn)t

where N is the number of data points, and T denotes the transpose of

matrix. Initial data are represented by the vector

Since the first breaks of head waves are function of the velocity

and the coordinates of the interface coordinates, unknown parameters

such as velocity and depth can be taken simultaneously to invert the

headwaves. In this case, solutions become unstable because the partial

derivative seismogram with respect to velocity and that of depth have

different values. To avoid this scale problem, we employed the

logarithmic variation developed by Madden (1972). Taking the

logarithmic variation of the Taylor series expansion of the model

response around a priori parameter p gives

log( Diitfj+Apj) ) = log( Ujiptj) )+ 2̂ aiog( u,W)J= i 91og( p] ) Alogpj (9-1)

log u.iti)~ & $ % gj(rf) )- A u,w 3( pJ ) Alogpj , (9-2)

and

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Pi dux Pz dux Pm dUiUi dpi Ui BPi Ui BPm

Pi du2 Pz du2 Pm dU2U2

£

u2 dPz u2 BPm

Pi BUh Pz suN Pm dUNUN dpi ■ Un dpz Un BPm

A\ogPi

A\ogp2

. (9-3)

Alogpu

where i = 1,2,3,—, /+1. , Allogpp^—pj, ■ j = 1,2,3,—,M N is the number of data points, and M is the number of parameters.

Unlike the usual residual vector and the partial derivatives, we have

scale free residual and partial derivatives defined as below

E = log.D—log U (or log-=), (10-1)

hPi dUjUi dpj

(10-2)

where, i = 1,2,3,—,iV and j = 1,2,3,—,MApplying standard least squares method to equation (9-2) and

adding damping factor 0 give the normal equation given below

(/7+#Mlog p= fE (11)

Solving Equation(ll) updating the parameter space by a general iterative rule, we can find the optimum parameters which minimize the residual between the field seismograms and model response.

-276-

3-3. Examples

3-3-1. Synthetic data

To verify the accuracy of traveltime of head waves, we compared

the traveltime curve computed by finite-difference method originally

developed by Vidale (1988) to the traveltime curve computed by

shooting method. Figure 5-24(a) shows a wavefront calculated by using

finite-difference method for a syncline model while Figure 5-24(b)

shows traveltime curves calculated by using shooting method and

finite-difference method. We can note that these curves match closely.

Figure 5-25 shows a fault model taken to compare the analytical

partial derivative wavefield to the partial derivative wavefield by

numerical differencing. By adjusting the difference interval of the

vertical coordinate of the point F in Figure 5-25, we calculated thez

partial derivative wavefield by central difference method, and compared

it to the analytic derivative wavefield. As shown in Figures 5-26 and

5-27, the analytic partial derivative wavefield matches with the

numerical partial derivative wavefield obtained by finite-differences.

Figure 5-26(a) shows partial derivative wavefield with respect to

vertical coordinate of point F at 40 Hz, whereas Figure 5-26(b) shows

the partial derivative wavefield with respect to the horizontal coordinate

of point F. Figures 5-27(a) and (b) show the real part and the

imaginary part of partial derivative wavefield with respect to the

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TIME (m

sec)

DEPTH (m)

DISTANCE (m)SOURCE

0 110 170 230 380

(a)

0 50 100 150 200 250 300 350130 -120 -

-1 10110-

Vidale(1988)'s method Shooting method

. •. -100

70 -

...... ! ....

DISTANCE (m)(b)

Figure 5-24. (a) Model for ray tracing Vidale's finite difference ray

tracing algorithm. (b)Comparison of travel times generated by Vidale's

method and shooting method.

-278

DEP

TH(m

)

50

\SOURCE20

DISTANCE (m) 100

v, - lOOOm/sec

150 200

30

F (100,30)

v. = 2000m/sec

too

Figure 5-25. A fault model taken to compare the analytic partial

derivative wavefield to the partial derivative by numerical differencing.

300 -1 FDM SOLUTION (5m) FDM SOLUTION (2m) FDM SOLUTION 1m) FDM SOLUTION (0.5m) ANALYTIC SOLUTION ■

200 -

100 -

2= -100-

-200 -

-300

RECEIVER POSITION

300 -| FDM SOLUTION (5m) FDM SOLUTION (2m) FDM SOLUTION 1m) FDM SOLUTION (0.5m) ANALYTIC SOLUTION

200 -

100 -

-100-

-200-

-300

RECEIVER POSITION (b)

Figure 5-26. Comparison between analytic and numerical partial

derivative wavefield (real components)of wavefield with respect to (a) z

-coordinate and (b) ^-coordinates of node point F (100,30) shown in

Figure 5-25 where frequency is at 40Hz.

-280-

velocity of the second layer. From the Figures 5-27(a) and (b) it is

shown that the amplitude of partial derivative wavefield increases as the

offset distance becomes longer. The partial derivative wavefield from 0

to 35th receiver position becomes null space, which indicates that the

refracted wave arrives first at 35th receiver position.

Figure 5-28 shows the inversion result for a curved layer model

with irregular surface. Input data of 6 shot records are used for the

seismic inversion. One hundred receivers are located with 3 meter

interval. As a starting initial model, three horizontal layer is taken as

shown in Figure 5~28(a). The unknown parameters for each layer are

the velocity of each layer and the nine interface coordinates of each

layer. In performing inversion, we started with 25 Hz low pass filtered

seismogram and brought up the frequency band at 5 Hz interval at

every 5th iteration. After 31th iteration, the velocity and depth model is

converged to the true model. Figure 5-28(a) shows the inverted

interface of the model, while Figure 5-28(b) shows the inversion result

of velocities. With increasing the frequency band, we decreased the

damping factor in solving the normal equation from 1 percent to 0.3

percent. Figure 5~28(c) shows the history of rms error as the iteration

goes. After 3 iterations, it is evident that the rms error reaches up to

25 percent of the initial rms error.

In order to test the pre-field data example, we used the synthetic

seismogram generated by solving 2 way scalar wave equation using the

finite-difference modeling technique. In this case, we generated the

synthetic seismograms of 11 shot and picked the first arrivals. Unlike

—281 —

FDM SOLUTION (50m' FDM SOLUTION (20m FDM SOLUTION (10m' FDM SOLUTION (05m ANALYTIC SOLUTION

— 10 -

RECEIVER POSITION (a)

FDM SOLUTION (50m FDM SOLUTION (20m' FDM SOLUTION MOm' FDM SOLUTION (05m ANALYTIC SOLUTION

— 10-"*"

RECEIVER POSITION

Figure 5-27. Comparison between analytic and numerical partial derivatives of wavefield with respect to second layer velocity in Figure 5-25 where frequency is at 40Hz. (a) Real component and (b) Imaginary component.

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0-1

lOOOm/sec

2000m/sec

True model

Initial model3000m/secFinal model

DISTANCE (m)

VELOCITY (km/sec)0 12 3

0 — — — — ... . i

20

!40

60-

80-

100-J

I

(b)

50Hz

O -

Sti 60-

M 40-

ITERATION NUMBER

(c)

Figure 5-28. Inversion result for a 3 layer model with irregular interfaces, (a) the true model and the inverted model, (b) inversion result of .velocity at x=200m, and (c) relative RMS error at each iteration.

-283-

the synthetic ray data example, there will be errors when the first

arrivals are picked from the synthetic traces. Figure 5-29 shows the

inverted interface and the velocities. The prominent feature of the Figure

5-29 is that when we have picking error or the true model is steep, we

might not have the good inversion result, though close to the true

model. However, the rough structure of velocity model is close to the

true model we might wish. Figure 5-30 is an example of seismic record,

which are obtained by the finite-difference solution of 2-D acoustic

wave equation for the model shown in Figure 5-29(a). we carried Out

inversion by using the picked data from the synthetic seismic record.

Interval velocities of each layer in the initial model are lOOOm/s,

2300m/s, and 3500m/s, respectively. From the Figure 5-29(a), we

obtained an almost exact convergence besides the area of having the

steep dip. Although error of the initial velocity is in the range from 10

percent to 20 percent, Figure 5-29(a) shows the result of velocity

inversion having errors in maximum 2 percent. In this example, we used

single frequency band of 20 Hz and the history of rms error is shown

in Figure 5-29(c).

3-3-2. Field data

After successful experiments on synthetics, we applied this inversion

scheme to field data provided by IPRG(Israel). Field seismograms are

acquisited by vibroseis source and 120 channel receivers are used.

Acquisition parameters are showed in Table 5-2.

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DISTANCE (m) ;gog

lOOOn/sec

True model

• • • • Initial model

— — Final model• 2000m/sec

VELOCITY (km/sec)0 12 3 40 | ii — i i i

IQ

20'

40

60

80'(b)

M i 1'1 I l 1 I | M 1 l I I l l-l |'l l I“f“| 'I I

ITERATION NUMBER

(c)

Figure 5-29. Inversion results for a 3-layer model with steep dipping interfaces, (a) inverted model, (b) inverted velocity at x=2000m, and (c) relative RMS error curve.

-285-

TIM

E (sec

)SOURCE

Figure 5-30. Synthetic seismogram generated by the finite difference

method where a source is located at x= 1500m shown in Figure

5-29(a).

—286 —

Figure 5-31 is a common shot stacked section(see Landa et al,

1995). The field data from 0 to 4 km on the section are used for

Table 5-2. Acquisition parameters of the IPRG field data.

Number of Shot 60 ea.Record Length 2 sSampling Interval 2 msOffset 40 mNumber of Group 120 ch.Group Interval 20 m

inversion. We picked the first breaks of 7200 traces consisting of 60

shot records by using the picking software for crosshole tomography.

The average velocity of surface layer is calculated using the

time-distance curve of direct wave. The velocity of first layer fixed at

1250 m/s. The initial value of velocities of the second layer and the

third layer are 2500 m/s and 4000 m/s, which are based on the average

of velocities calculated from the slope of time-distance curve. The

initial depth of interfaces of the second layer and the third layer, which

are composed of eight segments, are 30 m and 100 m, respectively. In

stead of the simultaneous velocity and depth inversion, we inverted the

velocities with fixing the interface, and obtained 2300 m/s and 4100 m/s.

After fixing the velocities of the layered model, we inverted interfaces

of the horizontal layers so that we can roughly estimate the geometric

structure of the model. The rough initial model found by pre-inversion

-287-

TlM

E(se

c)DISTANCE(Km)

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

(a)

DISTANCE! Km)

0 0.5 1.0 ■ 1.5 2.0 2.5 3.0 3.5 4.0

V1=1250 m/sec

V2=2300 m/sec

V3=4100 m/sec

(b)

Figure 5-31. (a) Real data common shot refraction stacked section. Refraction events are shown about 0.05s and 0.1s at the right of the section (Landa,1994). (b) inverted velocity model obtained by pseudo waveform technique.

-288-

TRA

VEL

TIME (s

ec

0.5 i

OBSERVED BREAKSFINAL UPDATED MODEL BREAKS0.4- -

0.3 -

0.2 -

0.1 -

100 110 120

RECEIVER POSITION

Figure 5-32. The picked traveltime and the first arrival time generated from the final updated model.

-289

process discussed above is used as the initial model for the

simultaneous velocity and interface inversion. After 16 iterations, we

could obtain the velocity model shown in Figure 5-31 (b). In this seismic

inversion, we used the 20 Hz low pass filtered seismogram. Figure 5-32

shows the traveltimes between the finally inverted model and picked

field seismogram, it is clear to note that these two traveltimes matches

well. Note that our inversion results are in a good agreement with the

results obtained by Landa et al.(1995) from the same data set.

3-4. Discussion

In this paper, we have proposed a new method for the determination

of velocity-depth model using the seismic refraction data. One of the

advantage over other methods is that, unlike the traveltime inversion,

this inversion technique can be performed in the frequency domain using

the damped Gauss Newton method. The shooting ray tracing allows us

to calculate the travel time efficiently for the complex layered structure

with irregular topography. In addition to this efficiency in the forward

modeling, the analytic partial derivative seismogram with respect to the

velocity and coordinates of the interface can be obtained by simple

algebra. Not only can we use the low frequency data when the initial

model is far from the true model, but also we can refine the resolution

with increasing the frequency band of the seismic data.

—290—

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441, 44f, 44*, 1995, 44444 444 *4*4 4 Mm 444414: 4444^444, 32, 22-36.

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APPENDIX

Chgromatograms of the Saturated Hydrocarbons &

Tables for Normalized n-alkane %

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FileOperator Acquired Instrument Sample Name Misc Info vial Number




TIC: E610.D

1000000 -

900000 -

800000 -

700000 -

Pristane600000 -

Phytane500000 -

400000 -

300000 -

200000 -

100000-

100.0090.0080.00rime- -> 70.0050.00 60.0040.00

File : Operator : Acquired : Instrument : Sample Name: Misc Info : vial Number:



C:\HPCHEM\l\DATA\POHANG\F\F110.D

Abundance TIC: F110.DPhytane

1600000 -

1400000 -Pristane

1200000 -

1000000 -

800000 -

600000 -

400000 -

200000 -

rime-->30.00 40.00 100.0050.00 60.00 70.00 80.00 90.00

332




C:\HPCHEM\1\DATA\POHANG\F\F120.D


900000 - Pristane

800000 -

700000 -

600000 -

500000 -

400000 -

300000 -

200000 -

100000 -

100.0090.0080.0070.0060.00Time--> 30.00 50.0040.00



C:\HPCHEM\l\DATA\P0HANG\F\F160-1.D

1

Abundance TIC: F160-1.DPhytane

1600000 -

1400000 -

1200000 -

1000000 -Pristane

800000 -

600000 -

400000 -

200000 -

rime--> 40.00 100.0050.00 60.00 70.00 80.00 90.00

334

File : Operator : Acquired : Instrument : Sample Name: Misc Info : vial Number:


Pohang saturated cut by TLC l



Pristane1200000 -

1000000 -

800000 -

600000 -

400000 -

200000 -

100.0090.0080.0070.00rime-->30.00 60.0050.0040.00

FileOperator : Acquired : Instrument : Sample Name: Misc Info : vial Number:


Pohang saturated cut by TLC.1


Abundance TIC: F280.D

PhytanePristane1800000 -

1600000 -

1400000 -

1200000 -

1000000 -

800000 -

600000 -

, 400000 -

200000 -

rime--> 30.00 90.00 100.0060.00 80.0040.00 50.00 70.00

336



C:\HPCHEM\1\DATA\P0HANG\F\F351.D

1


3000000 -Pristane

2500000 -

2000000 -

1500000 -

1000000 -

500000 -

100.00rime- -> 90.0080.0070.0040.00 50.00 60.00

337




1

Abundance TIC: F401.DPhytanePristane1800000 -

1600000 -

1400000 -

1200000 -

1000000 -

800000 -

600000 -

400000 -

200000 -

rime-->30.00 90.00 100.0040.00 80.0050.00 60.00 70.00

Z

338

C:\HPCHEM\l\DATA\POHANG\F\F489.DFileOperatorAcquired : 28 Jul 94 7:07 pm using AcqMethod P940726Instrument : 5989- InsSample Name:Misc Info : vial Number: l

Abundance TIC: F489.DPristane

Phytane

2500000 -

2000000 -

1500000 -

1000000 -

500000 -

100.0090.0080.0070.00rime--> 30.00 50.00 60.0040.00

C:\HPCHEM\1\DATA\P0HANG\F\F549.DFileOperator Acquired Instrument Sample Name Misc Info Vial Number



TIC: F549.DPhytanePristane

1200000 -

nooooo -

1000000 :

900000 -

800000 -

700000 -

600000 -

500000 -

400000 -

300000 :

200000 -

100000 -

rime--> 30.00 60.00 90.00 100.0040.00 50.00 70.00 80.00

340

File : C: \HPCHEM\1\DATA\P0HANG\E\E599-8.DOperator :Acquired : 14 Jul 94 8:37 pm using AcqMethod 9407Instrument : 5989 - InsSample Name:Misc Info : Pohang saturated cut by TLC Vial Number: 1

Abundance TIC: F599-8.DPhytane1000000 -

900000 -

800000 -

700000 - Pristane

600000 -

500000 -

400000 -

300000 -

200000 -

100000 -

100.0090.0080.0070.00rime--> 30.00 60.0040.00 50.00




C:\HPCHEM\l\DATA\POHANG\F\F667.D

Abundance 2400000 -

TIC: F667.D

2200000 :

2000000 -

1800000 -

1600000 -

1400000 -

1200000 -

Pristane1000000 -

800000 -

600000 -

400000 -

200000 -

100.0090.0080.00rime--> 70.0060.0040.00 50.00

i

z

342

* Normalized n-alkane , pristane & phytane distribution, E core______________ (%)

Depth E 120.6 129.3 E250.3 E279.9 E290.1 E310 E370.7 E429.4 E518 E560.3 E570 E610

C15 - * 2.38 - - - - - - - - - -

016 1.79 5.74 - - - - 5.12 2.67 6.40 3.63 1.59 -

017 4.27 8.24 2.72 - 2.88 - 4.94 3.43 6.30 4.37 4.54 1.49

Pristane 14.63 10.75 16.17 12.73 9.99 - 13.55 14.27 12.17 15.72 13:38 2.59

018 3.72 10.44 2.44 1.69 3.66 1.53 5.68 4.47 5.64 4.79 ' 4.97 2.51

Phytane 29.89 18.97 29.92 31.39 19.27 9.83 13,14 14.03 13.92 16.35 12.14 2.19

019 2.40 7.78 2.37 1.67 4.29 3.77 4.29 3.67 6.75 4.32 5.11 3.94

020 2.22 5.99 2.30 2.15 4.27 5.96 3.47 3.18 4.12 3.99 5.25 4.32

021 1.68 3.57 2.05 6.65 3.13 4.67 3.75 2.11 4.75 3.65 4.67 5.65

022 5.18 2.47 4.24 7.02 14.84 18.68 4.98 4.39 5.41 5.17 6.72 5.35

023 1.58 1.43 2.27 2.54 3.06 3.84 3.22 2.65 3.94 3.53 4.41 7.03024 1.06 0.95 1.79 1.47 2.85 4.16 3.51 2.70 4.73 3.56 4.04 6.14

025 2.37 1.41 2.10 2.28 4.21 3.52 3.18 2.42 5.05 4.27 5.29 8.74

026 1.20 1.13 1.55 - 1.62 1.25 1.99 1.60 3.14 3.39 3.11 7.51

027 3.61 3.04 3.15 3.91 6.74 4.49 6.67 5.69 2.71 3.08 5.56 10.41

028 4.49 2.49 5.92 6.43 6.57 7.57 3.34 6.22 1.21 1.46 2.72 5.95

029 6.46 5.56 9.35 6.76 5.57 10.22 7.51 6.38 3.22 5.11 6.52 8.76

030 6.63 3.43 9.98 10.40 3.01 13.75 3.41 9.56 3.81 4.36 3.80 6.88

031 6.80 4.25 1.70 2.89 4.05 6.75 8.25 10.56 6.73 6.41 6.19 6.85

032 - - - - - - - - - 2.83 - 3.68

343

I

* Normalized n-alkane, pristane & phytane distribution, F core

depth F110 F120 FI 60.2 F210 F280 F351 F401.5 F489.4 F549.7 F599.7 F667.2C16 3.50 3.54 0.00 3.26 " 1.99 2.03 0.00 2.99 0.00 0.00 0.00017 3.97 3.18 2.59 4.21 2.89 3.84 2.59 2.75 2.82 2.66 0.69

Pristane 12.12 11.70 9.19 • 13.67 10.35 12.64 9.19 13.41 16.36 4.29 2.09018 3.39 2.37 2.51 3.44 3.89 4.73 2.51 3.13 3.32 2.73 1.28

Phytane 19.60 14.04 10.25 17.29 10.72 15.15 10.25 13.24 17.05 12.23 0.00019 4.07 1.81 2.83 2.59 2.23 3.68 2.83 3.44 2.62 3.75 2.09020 2.94 1.87 3.05 2.92 2.96 2.99 3.05 2.82 3.51 4.65 2.49021 4.10 1.84 2.66 3.48 2.64 2.44 2.66 1.93 2.40 3.98 3.55022 5.46 2.31 4.38 5.10 4.05 4.82 4.38 3.48 4.10 4.54 4.97023 2.66 2.26 2.70 3.02 3.80 3.11 2:70 3.03 3.23 5.54 7.58024 2.16 1.86 3.21 2.89 3.17 2.73 3.21 2.73 3.22 4.80 ' 7.97

025 3.31 3.21 3.35 3.93 4.58 3.18 3.35 4.64 3.53 6.91 12.32026 3.48 3.22 2.49 1.69 1.48 1.99 2.49 2.05 2.70 4.97 8.44027 6.51 7.90 7.65 5.62 7.81 3.59 7.65 5.99 7.57 8.23 10.54

028 6.73 4.80 7.87 3.24 5.01 7.00 7.87 6.02 6.58 4.99 5.86029 10.31 14.79 15.27 11.08 13.99 7.93 15.27 11.26 8.05 10.99 11.09030 2.66 4.07 8.03 4.01 5.62 9.10 8.03 9.47 3.87 5.69 5.05031 3.02 15.24 11.96 8.56 12.80 9.06 11.96 7.64 9.06 9.06 13.96

direct hydrocarbon exploration and gas reservoir ... - osti.gov

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