USING A MULTIPLE LOG APPROACH TO EVALUATE GREEN

RIVER OIL SHALE IN THE PICEANCE CREEK BASIN

Rob Habiger

Phillips Petroleum Company157 GB

Bartlesville, OK 74004

R.H. Robinson

Phillips Petroleum Company8055 E. Tufts Ave. Parkway

Denver, CO 80237

ABSTRACT

A method has been devised for predicting modi

fied Fischer assay yield from conventional loqs in

the Piceance Creek Basin of Colorado. The tradi

tional approach for using well logs to predict oil

shale richness has been to relate one of the poro

sity tool responses to the modified Fischer assay.

This approach is unsatisfactory when there is sig

nificant lithology variation in the oil shale rock.

This study involves an exploration area of 86 Km?

in Rio Blanco County, Colorado. Fifteen coreholes

were drilled on the property to provide the data

base for development of the method. The method

describes the rock in terms of a simple litholoqy

model consisting of: 1) carbonate, 2) clay, 3)

porosity, and 4) organic matter. Since each of

these quantities can vary independently, the simple

approach of using only one porosity tool does not

work. Instead, the density, sonic, and resistivity

logs are used in combination to isolate the amount

of organic matter by predicting the modified

Fischer assay. Problems related to log data vari

ability and core data quality will also be dis

cussed.

INTRODUCTION

A suite of conventional wireline logs is used

to predict oil shale richness on a property of 86

Km2 in Rio Blanco County, Colorado. The project

location is shown in Figure 1. Althouah there

exists a variety of correlation equations in the

literature relating well log measurements to oil

yields in Colorado and Utah, none are satisfactory

in predicting yields in this property. The assay

prediction method we developed uses data from 15

wells that were both cored and logged. The method

was applied to predict yield in 7 wells that were

only logged, and were previously unusable for

resource evaluation and mine plannina. For one of

these holes the log data was too poor to use.

An additional result of this work is a regres

sion equation that predicts true bulk density of

the rock from logged density. This relationship is

not straightforward since well logs give an indi

rect measurement of bulk density and overestimate

it in organic-rich material. In addition, logged

density exhibits greater variability than the core

measured density. These factors require that the

log data be carefully adjusted and correlated with

the core data to provide an accurate bulk density

for use in mining calculations.

REVIEW OF PRIOR WORK

Table 1 lists most of the published work con

cerning use of well logs to predict oil shale yield

or more specifically, the Modified Fischer Assay

(MFA) in Colorado and Utah. In each case, the data

base for these correlations is quite limited. Fur

ther, the correlations are based upon a sinqle

variable, usually the logged bulk density or forma

tion velocity. Another point, more subtle but nev

ertheless very important, is that some correlations

were developed from log data while others were

developed from core data.

In the past, any of three porosity logs (den

sity, sonic, and neutron) were used independently

to predict oil shale yield. Any of these loos

45

MAHOGANY SHALE

PROJECT>Nfc**e

*/

COLORADO

FIG. 1

Locations for literature correlations (see Table 1)

reference (3) is in the Piceance Creek Basin (exact location unknown)

IDAHO

UTAH

WYOMING

Green River Basin

COLORADO

ICreek

FIG. 2

46

TABLE 1

Literature Correlations for Predicting Oil Yieldin Colorado and Utah Oil Shales

REFERENCE EQUATIONA

(1) Smith (1956):

(a) Y =

(31.563)(p)2-(205.998)(p)+(326.624)(b) Y =

(22.881)(p)2-(166.8)(p)+(280.439)

(2) Bardsley and Algermissen (1963):

Y =

Y

= (-66.41)(PH(171.23)= (41.01xlO-4(At)2-(16.71)

(3) Tixier and Alger (1967):

Y = (-59.43)(p)+154.81

(4) Cleveland-Cliffs (1975):

Y = (496.325)(p)-^.(285.176)Y = (l56.767)(At)l-8.(29.1703)

REFERENCE LOCATION6

(1) (a) Garfield County, Colorado

(b) NE corner of MSP property

(2). .Unita Basin, Utah

(3) Piceance Basin, Colorado

(4) Unita Basin, Utah

REFERENCE DATA TYPE DATA QUANTITY YIELD RANGE

(1) Lab Grain Density 1 corehole 3-78 GPT

(2) Schlumberger Log 1 corehole 5-80 GPTC

(3) Schlumberger Log 1 corehole 2-37 GPT

(4) Birdwell Log 3 coreholes 0-80 GPT

A. Units are: p in gm/cc, At in microsec/ft, Y in gal/ton.

B. See Figure 2 for relative locationsC. Data were not fit well at the higher yields (underpredicted).

TABLE 2

Summary of Log and Assay Data

DRILL HOLE LOG DATA

BPB SCHLUMBERGER PHILLIPS R&D

-MFA

P-1 X X

P-2 X

P-3

P-4

P-5 .X

P-6 .X x X X

P-7 .X

P-8 .x

P-9 .x X

P-10 .x X X X

Sungas FC-36B-1-100.

P-12 .X X

P-13 .X X

P-14 .X X

P-15 .X X

P-16 .X X

P-19 .X X

P-20 .X X

P-22 .X X

P-27 .X X

P-28 X

Sungas 24-1-100 X

A. The log data quality was too poor for use.

47

would work well if the rock consisted of only two

components, a host matrix and orqanic matter. The

organic matter volume fraction would represent

porosity to these logs. A single variable correla

tion could then be made between MFA and loqged

porosity. Althouqh this single variable approach

may work for limited data, it is inadequate for our

project area. In Figures 3, 4, and 5, we plot MFA

against each porosity loq. The data for these

plots come from coreholes spanning the entire prop

erty. These plots are a 4% sample taken from all

the log and core data. Our correlations include

the entire data base which is described in the next

section. In the Figures, note the considerable

point scatter due to a variety of factors. Miner-

alogic variations are probably chief amonq these

factors, but other factors such as hole condition

and log calibration are also important. Another

scatter plot, Figure 6 shows MFA plotted against

logarithm of resistivity. There is evidence for a

relationship but point scatter is again very large.

It is quite clear that none of these logs, by them

selves, are sufficient to predict MFA with the

desired accuracy. In a later section we will show

how log parameters can be combined to predict MFA

quite accurately.

Density is the most important parameter for

predicting oil shale richness, but one must ensure

that there is no ambiguity in its measurement.

There are three different densities reported in the

literature in connection with predicting yields

from oil shale. The first of these densities is

grain density which is measured from ground rock

samples (Smith, 1956). Similar work has been done

at the Phillips Research Center (Kokesh, 1982).

The results of the Phillips work are shown in Fiq-

ure 7 along with Smith's correlation. The two

pieces of work agree quite well. However, this

grain density cannot be applied directly to a mea

surement made from a borehole. The borehole mea

surement more closely approximates a bulk density

which in turn is related to the grain density by

equation (1) below.

Pb= U -4>) P9 + ?Pf f1)

where

<}> = fractional porosity

pd= bulk densiy of whole rock

pg= grain density of ground rock

Pf= density of the fluid in the

pore space

Bulk density is the density of the total rock

which includes any pore or fracture space. This

space is usually filled with a fluid such as water,

oil, or gas. When the rock is ground, any fluid

escapes and density of the ground rock is grain

density. The difference between these two den

sities can be quite significant as seen in the sec

ond track of Figure 8. These densities were used

to calculate porosity in the third track using

equation (1). Incidently, notice that the resis

tivity indirectly correlates with the porosity.

This behavior is consistent with the model that

porosity is salt water filled. In addition, the

confusion regarding density is further complicated

by the density logging tool measuring an"apparent"

bulk density. This apparent density can varysiq-

nificantly from the true bulk density in orqanic

rich material (Habiger, 1981). This topic will be

discussed in detail in a latter section of this

paper.

DATA BASE

The data for this study can be separated into

three sets. One set comes from our prospect's 1980

exploration season, a second set from the project's

48

80 r

60

co

.2 40>-

20

0

1.8

*x x

$ x x x xxxx * * * *

X X j(X

Xx

* xXx<x xxx,

xxiv x* xx XJC xr

* ic * * x x. X

-x xv

xx x

- -

X T^XXx * xX MX Xjc X X

*

XJ* * * x

XJ(xx

**^ X*X*X

x

"x x

J** x "x*** *

*

x

xx'xx* v 3c x& x *"*

.>

xx

Tv ^J"* "xw1 , x x X^ ^.x^ *X| g ^x/,

2.0 2.2 2.4

Density (gm/cc)

FIG. 3

2.6 2.8

80r

c

o

T 60

CO

a>

?40

20

130

* X.

K *X

X X

X x

*

x*.

**K

# X*** "-*

x T"

x_

^ # * XX

x x****

X

^ xv -

XxX

Xx

110 90 70

1/v (microsec/ft)

FIG. 4

50 30

49

80 r

c

o

CO

60

2

.2 40

20

X

X

X

X X

X

XX

**

*X

x*t x

x xxXX X

*

X

X>oc x

X X X

X

xxx

xX X

"

x x *< x

x>cx

x X x

x #x

xxxx

x Xx Xx

*

X X X X

**"

XX

V

^X

x x *xxx

* **.X

x X xx

x

x*

x

x*X

Xx x Xx

xx

xX

xx

X

* x x

xx Xx X

*X *

I 1 Xj

xx *xx

_.xx xi xx, x, X 1

40 80 120

Neutron (standard neutron units)

FIG. 5

160 200

80

o4 60">

CO

T3

0)

>-40

20

oL

5

x5 xr

.XXXX X \ jf

*"

X *x.

xx \

x *

xXX..* x#Xxx* x

XX ""i'

x xxxx

xx

~

xxx

X"Xxxl " ^l* *x"<A

"X

x x* J" * X

X * XXx x o,

* X X*w

XV%X*^

xxxx x x

xJ<

xx5c .,

4 3 2

Log(R) R in ohm meters

FIG. 6

50

80 rSmith's equation (b)

c

o

CO

O)

35

0)

1.8 2.0 2.2 2.4

Grain Density (gm/cc)

FIG. 7

2.6 2.8

FIG. 8

51

1981 exploration season, and the third set from two

Sungas drill holes that were loqqed by Phillips.

The logging service company was the British com

pany, BPB Instruments, Inc. In addition, Phillips

Research Center personnel recorded full sonic wave

forms in three holes. Our full waveform sonic log-

ging technique requires that Schlumberger Well Ser

vices equipment be on location concurrent with the

Phillips operation. While there, Schlumberger ran

a limited log suite for those three holes. This

additional logging provided a useful check on qual

ity and calibration of the BPB loqs. All core was

analyzed by the MFA and there were some special

analyses performed on selected core.

ADJUSTMENTS TO LOG DATA

Averaging and Caliper Log Analysis

Core samples were assayed on 1-foot intervals

in the 1980 season and on 2-foot intervals in the

1981 season. In addition, log data were recorded

digitally every 6 inches. For the purpose of mak

ing correlations, a filter function averaged the

log data so that it would match the vertical reso

lution of the core data.

The caliper log was used to eliminate those

parts of the drill holes too rough for an accurate

log density measurement. Unfortunately, density

log is both the most important log and the one most

affected by borehole rugosity. The density tool

must be held against the borehole wall during log

ging. Borehole irregularities cause gaps between

the tool and the borehole wall which leads to erro

neously low density values. The caliper log

locates these irregularities and the predicted

yields from those depths are not used in the corre

lation.

Log Calibration and Normalization

Ideally, all logging tools would be properly

calibrated and repeatable. However, Figure 9 shows

a series of density log histoqrams that exhibit

shifts of as much as 0.15 gm/cc relative to each

other. Lange (1980) reported that the density log

is sometimes miscalibrated and this could explain

the observed shifts. In addition, there could be

other reasons for the shifts, such as hole-to-hole

variations in rock type. Table 3 shows the results

of an analysis of variance for the various logs and

yields among the 1981 core holes. All the logs and

yields have statistically significant variability

from hole-to-hole, but the variability of the den

sity log is by far the largest. Without studying

the rock and practices of the logging company in

detail, we cannot explain the source of this large

variability. Nevertheless it is critical to cor

rect for this large density variability since den

sity greatly affects predicted yield. After trying

many correction methods, we settled on the simple

approach of transforming to a new variable.

Density Variation = (log density)-(loq densitymean) (3)

In this equation, the log density mean is calcu

lated for each hole and listed in Table 4. Unfor

tunately this transformation removes all the den

sity variability, even that which is due to average

yield variation. It is acceptable though because

the grade variability is a small part of total

variability.

DATA INTERPRETATION AND ANALYSIS

Geologic Model

We looked for some natural divisions of the

data which would reduce the variability between MFA

52

X OF

SAMPLE

5.00 +

2.50

0.0

5.00

2.50

0.0

2.50

0.0

5.00

2.50

0.0

5.00

2.50

0 +...

1.80

Log Density Histograms (gm/cc)

P-28 **

*****

*******

* * * ** ************

** a*****************************************

P-10* * * *

** * **

P-3

****

** * *** ***

* ** *****************************************

P-14

* * * * *

* * ** ** * *

* * * *

********* ******** * **

* ******************************** **********

* *** ********************************************* v?*

P-27

** * * *

* * ***************************************

1.90 2.00 2.10 2.20 2.30 2.40 2.50 2.6

FIG. 9

53

TABLE 3

Analysis of Variance procedure on Log and Yield Data

Data Base: 1981 Upper Zones

Model: log density = well #

Source DF Mean Square

Model 4

Error 901

0.740

0.0095

F Value

77.93

PR>F

0.0001

Model: log A t = well #

Model 4 546.976

Error 901 133.410

4.10 0.0027

Model: logarithm (log resistivity) =well #

Model 4 3.914 9.62

Error 901 0.407

0.0001

Model: Yield = well #

Model 4 245.330

Error 901 93.082

2.64 0.0329

Data Base: 1981 Upper and Lower Zones

Model: log density = well #

Model 9 0.688

Error 2076 0.010

Model: Yield = well #

Model

Error

9

2076

303.998

87.618

66.80

3.47

TABLE 4

Mean Logged Densities by Core Hole

0.0001

0.0003

Hole

P-12

P-13

P-14

P-15

P-16

P-19

P-20

P-22

P-27

P-28

Mean Density

2.32 gm/cc

2.19

2.35

2.30

2.28

2.22

2.25

2.21

2.23

2.20

54

and well logs. A useful, yet simple, division sep

arates the oil shale strata into an upper and a

lower zone. This division is easily picked from

logs as the example in Figure 10 shows. Even

though the densities in the two zones are generally

the same, the lower zone has lower resistivities

and slower sonic velocities. Although mineraloqy

of the western oil shale is probably quite compli

cated, a relatively simple model can be used to

interpret the log data and arrive at an oil yield

prediction. The vertical division that is so

apparent from the logs provides the key to this

model. We will refer to this division throughout

this report by an upper and lower zone.

The mineralogic model consists of four compo

nents: 1) carbonates, 2) clay with associated

bound water, 3) porosity, and 4) orqanic matter.

Figure 11 shows neutron-density crossplots for the

upper and lower zones of P-10. In the upper zone,

the points fall between limestone and dolomite

matrix lines. In the lower zone, the points shift

in the direction indicating addition of clay to the

matrix. This interpretation means the lower zone

has considerably more clay than the upper zone and

is supported by a number of observations:

1) A basin-wide geologic feature, the Blue

Marker, separates the strata into the Parachute

Creek Member and the Garden Gulch Member of the

Green River Formation (Newman, 1980). The two mem

bers are distinguished by a visible change in clay

content. X-ray diffraction and thin section data

for hole P-6 are listed in Table 5 and support the

model of a carbonate-clay matrix that increases in

clay content in the lower zone (Basick, 1982).

2) Figure 12 compares histograms of the amount

of water collected during the Fischer Assay for

corehole P-15 in upper and lower zones. The

increased amount of water in the lower zone is

probably due to the presence of bound water in the

clays. This increase explains why the lower zone

is characterized by a lower resistivity.

3) It is also reasonable to expect that the

clay causes the compressional velocity to decrease

in the lower zone. This effect occurs as shown on

the sonic log in Figure 10.

4) Additional evidence for increased clay con

tent in the lower zone is provided by full waveform

sonic data taken by Phillips R&D personnel (Basick,

1982). Figure 13 shows the full waveform data for

hole P-6. Two characteristics stand out in the

waveforms. The first onset, which is the arrival

of the compressional wave, shows decreased velocity

in the lower zone which was apparent in Fiqure 10.

In addition, the shear wave disappears in the lower

zone. The clay index (to be discussed later) cor

relates with both effects. Clay decreases the com

petency of the rock resulting in slower compres

sional and shear velocities. In the clay-rich

zones, shear velocity most likely becomes less than

borehole fluid velocity which results in no

recorded shear wave. This effect most probably

causes the disappearance of the shear wave. In

addition, both compressional and shear waves will

be more highly attenuated in the softer rock. The

interval at 795 is uncharacteristic of the lower

zone in that it has a low clay content as evidenced

by the clay index, the compressional onset time,

and the strong shear wave amplitude.

Clay contains water that is chemically bound

in the rock. But there is also a considerable

amount of water that is "free", i.e. contained in

the rock pore space. The work of Kokesh (1982),

which compared bulk and grain densities, showed

clearly the existence of porosity. This porosity

55

P-15

FIG. 10

TABLE 5

X-Ray Diffraction Data for P-6

Depth Quartz

25

Peak

Analcime

4

He ights

Ankerite

Dolomite

30

Calcite

80

Area

Illite

Mixed Layer

2,8A

Pyrite

7

Peak

Albite

Heights

Muscovite

0

X

11

Y

326 4 0

342 35 41 34 3 6 5 6 0 5 0

668 23 14 45 19 19 0 3 0 0 0

742 32 9 9 20 24 14 2 10 18 0

753 7 0 79 0 1 0 3 0 6 1

807 43 0 21 0 5 0 3 0 16 11

1019 85 0 4 16 24 0 9 10 11 8

1106 24 11 55 0 40 10 5 0 0 0

Numbers s

suspected

hould only be compared within a

to be K-Feldspar. Samples at

mineral type. Minerals X and Y are not identified although

^53 and 807 are not typical of the interval.

X is

Montmorillonite

56

P-10 Schlumberger log data

*0 6o

Neutron Porosity (%)

(a)

upper zone

o 22

/ ".lower zone

/ " ****

"

.

/ m f* mJZ^n**

/ ** AS**"

'""%/** * #c' >v

V*

/ "/",

V

V6./

I i i i i i i i i

Neutron Porosity (%)

(b)

FIG. 11

57

P-15

upper zone

X OF

SAMPLE

+ + + + + ? + ? ? ?? 0.0

B.BB .50 1.00 1.50 2.00 Z.50 3.00 3.50 4.00 4.50 5.00

.50 1.00 1.50 Z.00 2.50 3.00 3.50

Water from Fischer Assay (wt %)

4.00 4.50 5.

FIG. 12

58

Full Waveform Sonic

ml

50 0

upper zona

lower zone

700

800

.'h

"IMffifi*.:.:.Zk r.i, . 1; -;r. n r*

mmm

P-6

FIG. 13

59

cannot be distinguished from organic matter by the

standard porosity logs (density, sonic, neutron).

However, the porous intervals generally have low

resistivities which suggest that they are filled

with conducting water.

Log Interpretation

The density log would probably be a reliable

indicator of organic matter content if it were not

for the porosity variations. Grain density mea

surements indicate that the clay and carbonate have

approximately the same density so that the volume

fraction of organic matter (Vo) could be approxi

mated from the bulk density (pb) and matrix density

(pg).

Vo = (pg-pb)(pg-D"

(4)

In this case, the rock-matrix density is fairly

constant no matter what the mixture of carbonate

and clay. However, porosity causes density to vary

considerably and the simple relation in equation

(4) is not valid. The resistivity loq can be used

to identify the porous intervals. Unfortunately,

the resistivity log cannot tell the difference

between porous intervals filled with free water and

clay rich intervals.

This inability to distinguish porosity and

clay on the resistivity log can be demonstrated by

a series of crossplots. Figure 14 compares cross-

plots of yield versus density variation with dif

ferent resistivities for a composite of the upper

zone. Notice that the hiqhest resistivity inter

vals show a good yield-density correlation. These

intervals have very little clay or porosity. As

the resistivity decreases, points scatter towards

lower densities. This is due to increased poros

ity. Still there are some points that remain on

the correlation line even at the lower resistiv

ities. These points owe their low resistivities to

clay and not porosity.

The clay-porosity ambiguity can be resolved if

the clay content can be determined independently.

Probably the most common clay indicator used in log

analysis is natural gamma ray activity. But for

this project, the qamma ray curve has much charac

ter but does not appear to be related to clay con

tent. Therefore it is necessary to use other logs

to determine clay content, and we have already men

tioned that the sonic and neutron logs are affected

by clay. Unfortunately, the amount of neutron data

on hand are very limited since no neutron logqing

was done in 1981 and the 1980 neutron log data are

incomplete. Consequently, we chose the sonic log

for clay volume determination. Figure 15 displays

sonic-density crossplots for two different resis

tivity ranges while the higher resistivity range

excludes intervals with significant clay content,

the lower resistivity range includes intervals with

either clay, porosity, or both. These data are

taken from a composite of the upper zones. The

correlation line established from the clay-free

graph is drawn on the low resistivity graph. Those

points that still fall on the correlation line are

interpreted as clay-free, porous intervals. On the

other hand, points that fall to the lower right of

the line are intervals with varying amounts of

clay. Using the sonic-density crossplot, we assign

a clay index to each point based upon its departure

from the clay-free line.

CI = (log At) + (127.31)DV -84.84 (5)

We found this index to be most useful in the upper

zone to distinguish porosity from clay in a predom

inately carbonate matrix.

60

100 r

80

~ 60

> 40

20 -

high resistivities

4 < log R ^ 5

"\

-

-

- ^^C

i i i ^S. _i 1 1

-0.5 0.3 -0.1 0.1

Density Variation (gm/cc)

0.3 0.5

100

-0.5

low resistivities

0 ^ log R S 2.5

0.3 -0.1 0.1

Density Variation (gm/cc)

FIG. 14

0.3 0.5

61

40 r

60high resistivities

80

100

120

140

-0.5 -0.3 -0.1 0.1 0.3

Density Variation gm/cc)

0.5

40

60

low resistivities

80

Ew 100

120

140

y s*Vl&" cav

*"""""

" 1

-0.5 -0.3 -0.1 0.1 0.3

Density Variation (gm/cc)

FIG. 15

_i i

0.5

62

Our interpretation is supported by showing in

Figure 16 that the points of Figure 14 with the

highest clay indices fall on the correlation line

while the points with the lowest clay indicies

(porous intervals) fall off the correlation line.

Clay content by itself does not affect the

density-to-yield correlation. It is porosity that

has the most adverse affect on the correlation.

However, porosity can only be detected from logs by

measuring formation resistivity, and resistivity is

strongly affected by clay. Therefore, the presence

of porosity requires that clay content be quanti

fied so that resistivity can then be effectively

used to correct for porosity on the density log.

In effect, the clay index describes how much of the

resistivity decrease is due to clay and how much is

due to porosity.

By using density, sonic, and resistivity logs,

we have the information necessary to identify indi

vidual contributions from each of four main compo

nents: 1) carbonate, 2) clay, 3) porosity, and 4)

organic matter. The remaining task is to find a

suitable mathematical model for expressing resis

tivity as a function of these rock components. One

approach would be to calculate porosity as true

porosity + organic-matter volume fraction from the

density log. Then this porosity and clay volume

fraction could be applied to a standard loq analy

sis used for oil & gas applications. Next an "oil

saturation"

would be calculated which could be

related to the MFA assay. However, this method

requires estimation of five parameters: two forma

tion factor constants, a saturation exponent, an Rw

and a parameter relating clay volume to clay index.

Since this method is not a standard application of

accepted oil & gas techniques, we were hesitant to

assume that common values for these parameters are

acceptable. Consequently, we feel that it is much

more scientifically sound to simply do a multiple

linear regression using density variation, loga

rithm of resistivity, and clay index. This three

variable regression is used for the upper zone. In

the lower zone, good prediction was obtained using

only two variables, density variation and logarithm

of resistivity. The results of these correlations

using the 1981 core data are:

Upper Zone

Y = (-74.37)(DV)+(7.86)(log R)+(0.5)(CI)-9.65 (6)

Lower Zone

Y = (-81.58)(DV)+(4.70)(log R)+9.36 (7)

These equations predict yield in gallons/ton when

the variables are calculated as described in equa

tions (3) and (5) with the log measurements of den

sity, At, and resistivity in gm/cc, microsec/ft,

and ohm-m. Figure 17 graphs core MFA versus log-

predicted yields using equations (6) and (7) for

the 1981 data. Points eliminated from the regres

sions come from three sources: intervals where the

caliper indicates an unreliable density log, one

large interval in P-16 where MFA do not depth-

correlate with any logs, and short intervals where

MFA are insufficient to be representative of log

measurements.

PREDICTING TRUE BULK DENSITY

The density log indirectly measures true for

mation bulk density. Actually this loq value is an

apparent density that is generally hiqher than the

true bulk density in organic rich rock. This sec

tion describes the relationship between logged den

sity and density measured on core samples. Because

our depths are shallow, we assume that laboratory

values represent the in situ density very closely.

Extensive laboratory bulk density measurements were

63

uu

80

low resistivities

high clay indicies

60

"\

\

40

X

20

0 ' , ,

*

.

" 7\T-0.5 -0.3 -0.1 0.1 0.3

Density Variation (gm/cc)

0.5

100

low resistivities

80 low clay indicies

60

a

a'

2 ^x

>40

20

,^

0 i i Vb

# ^n.

-0.5 0.3 -0.1 0.1

Density Variation (gm/cc)

0.3 0.5

FIG. 16

64

20 40 60

Log Predicted Yield (gpt)

100

FIG. 17

2.8 r

P-15

2.6

2.4 -

2.2

2.0

xxx

&yXX Mx**<

**Jr*

NsxJRx $X f X J&***m

xvx **jyiJFxxfSS

x x^Sxx%JBJkx

)t apocjtiry,..^ l^Fxx

xx5c SiAxx i"xfMxX

>x*?)W~X

X3*v*tt<WS*x*xx><

XX / X X< XX X M

3KX.3O0OCXX X

X Tf/T^PBysc $

/xA x$ xxx

_yx J*b X XX

s ******* xx*"

* r $x*Vx \*$XX XXM

xX

I

XjSX x*

x"x? xx*xm

x** x

x*xxx

X

5xx sX XXX X V,

xwx)T KX*

X

1.8

1.8 2.0 2.2 2.4

Log Density (gm/cc)

FIG. 18

65

2.6 2.8

made on two foot core intervals from holes P-15,

P-22, P-27, and P-28.

An example of the relationship between loq

density and bulk density is shown in Fioure 18.

The relationship between bulk density and loo den

sity is further complicated by the variability in

log densities that was discussed earlier. An anal

ysis of variance for the log and lab densities is

listed in Table 6 and show that log data hole-

to-hole variability is greater than lab density

variability even though the lab density variability

is significant. Figure 19 plots log density versus

lab density for the entire data base. The log den

sity variability scatters the points so that the

departure from the linear trend at low density is

less apparent than it is in Figure 18. The corre

lation for predicting true bulk density increases

by using the same density variable qiven in equa

tion (3). Fiqure 20 shows the results for thistransformation. The prediction equation is

bulk density = (2.227) + (1.189)(DV) (8)

TABLE 6

Analysis of Variance Procedure for Log and Lab Density

Data Base: Coreholes P-15, P-22, P-27, P-28

Model: Log density = well #

grateful to Phillips Petroleum Company for permis

sion to publish these results.

Source

Model

Error

DF Mean Square F Value PR>F

3 1.357

1718 0.011

121.72 0.0001

Model: Lab density = well #

Model 3 0.358

Error 1718 0.015

23.28 0.0001

ACKNOWLEDGEMENTS

We acknowledge the assistance of J. T. Basick

in providing full waveform sonic data and analysis

of the x-ray diffraction data. Hans Schmoldt pro

vided much supporting technical material. We are

REFERENCES

Bardsley, S. R. and Algermissen, S. T., 1963,

Evaluating oil shale by log analysis: Journal

of Petroleum Technology, vol. 15, no. 1, p. 81-

84.

Basick, J. T., 1982, Personal commun.

Berge Exploration, Inc., 1980, Mahogany Shale

Project: Report for 1980 Field Season (Unpub

lished Report).

Cleveland-Cliffs Iron Company, 1975, Geophysical

logging report for the White River Shale

Project (Unpublished Report).

Habiger, Robert M., 1981, Phillips Petroleum

Company Research and Development Report (Unpub

lished).

Kokesh, Fritz, 1982, Personal commun.

Lange, W. H., Jr., 1980, SPWLA ad hoc log calibra

tion committee report: Porosity log calibra

tions: The Log Analyst, vol. 21, no. 2, March-

April, p. 14-19.

Newman, K. R., 1980, Geology of oil shale in the

Piceance Creek Basin, Colorado: Rocky Mountain

Association of Geologists Symposium, p. 199-203.

Smith, John Ward, 1956, Specific gravity-oil yield

relationships of two Colorado oil shale cores:

Industrial and Engineering Chemistry, vol. 48,

no. 3, p. 441-444.

Tixier, M. P., and Alger, R. P., 1967, Log evalua

tion of nonmetallic mineral deposits: SPWLA

Symposium Paper R.

66

2.6

E 2.4

2.2

2.0

P-22

P-27

P-28

1.8

1.8

'xlLxSx

<x#*

*%xxxxKxx3*xV*Sc

x

x x S8 y-JTxjC***"*

*v*

/*Jx X*

/

2.0 2.2 2.4

Log Density (gm/cc)

FIG. 19

2.6 2.8

2.8 r

-0.3 -0.1 0.1

Density Variation (gm/cc)

FIG. 20

67

0.5