thesis topic an integrated petrophysical study using well logging data for evaluation of a gas field...

Thesis Topic

An Integrated Petrophysical Study Using Well Logging Data for Evaluation of a Gas Field in

The Gulf of Thailand

Presented by Thoedpong WitthayapraditST105514

Date 18 May 2009

Committee member : Dr. Pham Huy Giao (Chairman): Dr. Noppadol Phien-wej: Dr. Le Hai An

Outline

Introduction Objectives Scopes of work Literature review Methodology Results and Discussions Conclusions and Recommendations Q & A

Abbreviation

ANN Artificial Neural NetworkGR Gamma Ray logDT Acoustic logNPHI Neutron porosity logRHOB Bulk density logRESD Resistivity log- deep investagationRESM Resistivity log- medium investigationRESS Resisitivity log- shallow investigationIP Interactive PetrophysicsMATLAB MATrix LABoratoryMMSCF Million Standard Cubic FeetMTJDA Malaysia-Thailand Join Development Area

Introduction

The understanding of reservoir rock characteristics by formation evaluation is needed to determine producible potential of petroleum extracted from reservoir.

Artificial Neural Networks (ANNs) have been developed to predict some

petrophysical parameters. The study of well logging interpretation and flow unit

characterization enhances the ANN prediction performance.

Fig 1 Application of ANN of well logging (http://www.neuralog.com/)

Objectives

The objectives in this study is ;

Application of the backpropagation ANN technique to predict the porosity and permeability

of gas reservoir in North Malay basin, Gulf of Thailand by using of well logging and core data

that can be used for formation evaluation in the study area.

Scopes of Work• Collection of data, i.e. well logging, core analysis and geological data

• Perform a well logging interpretation using Interactive Petrophysics software.

• Flow unit characterization from core analysis data.

• Review on the ANN methods and its application in parameter prediction based on the well logging data.

• ANN training and testing with data sets from all available data, data selected from interpreted reservoir zone and data from flow unit characterization.

• Perform a trained ANN analysis using MATLAB on data sets in integration with the core analysis data to predict porosity and permeability.

• Compare porosity and permeability from derived well logging, core analysis and ANN prediction

• Natural gas situation and statistic in 2007• Area of study• Formation evaluation

– Coring and core analysis– Well logging

• Flow unit characterization• Artificial Neural Network (ANN)

Literature ReviewThe literature review is following:

Fig 2 Proved natural gas reserves at end 2007 (http://www.bp.com)

Fig 3 Distribution of proved natural gas

reserves (http://www.bp.com)

Natural gas situation and statistic in 2007

Fig 4 Natural gas consumption by

area (http://www.bp.com)

Fig 5 Natural gas production by area (http://www.bp.com)


Fig 6 Natural gas consumption per capita (http://www.bp.com)



Fig 7 Natural gas price historical (http://www.energyshop.com)

The gas field is geologically located in the North Malay Basin.

Area of study

Fig 7 Malay Basin province (Michele,2002)

North Malay basin characteristics

-Depth : Thick sediment > 8 km

-Geological age : Tertiry

-Geological structure : horst and graben

fault

-Source rock : Shale and siltstone

-Reservoir rock : Sandstone

Area of study

Fig 8 The comparison of stratigraphy from difference area in Malay Basin

Source: Michele B. (2002),

Formation Evaluation

The use and interpretation of several tools and methods that are capable of locating and evaluating

the commercial significance of petroleum in the rock. (Lynch,1962)

The formation evaluation tools

• Coring and core analysis

• Drilling fluid and cuttings analysis logging

• Well Logging

Electric logging

Radioactive logging

Acoustic velocity logging

• Drill Stem Testing (DST)

Coring and Core Analysis

Fig 9 Core sample

(http://www.intertek-cb.com)

Coring is integration part of formation evaluation which direct

measurements are made.

Routine Core Analysis (RCAL) Special Core Analysis (SCAL)

•Porosity•Grain density•Absolute permeability•Water and oil saturation•Water salinity•Lithologic description•Core Gamma Log•Core Photography

•Capillary pressure•Relative permeability•Wettability•Petrography- thin sections and SEM•Damage assessment tests•Electrical properties- n, m and F•Mechanical properties•Acoustic velocity•NMR •Mineralogy- QXRD•Completion testing

Table 1 petrophysical parameters which can be determined from core measurement

Well Logging Well logging can measure and

record formation properties continuously versus depth.

Formation Measurement Parameters

- Electrical resistivity- Bulk density- Natural and induced radioactivity- Hydrogen content- Travel time of sonic wave

- Porosity (primary and secondary)- Permeability- Fluid saturation-- Hydrocarbon type- Lithology- Formation dip and structure- Sedimentary environment- Elastic modulus

Table 1 Measurement of formation

Fig 10 Well logging measurement(http://www.laynewater.com)

Flow unit characterizationThe flow units are the resultant of the depositional

environment and diagenetic process. Many authors defined flow unit as follows(Tiab et al,2004);

- A flow unit is a specific volume of reservoir, composed of one or more reservoir quality lithologies

- A flow unit is correlative and mapable at the interval scale.- A flow unit zonation is recognizable on wire-line log.- A flow unit may be in communication with other flow units.

Flow unit characterization factors• Reservoir quality index (RQI)

Based on the Kozeny-Carman equation (1939), Amaefule et al. (1993) introduced the concept of reservoir quality index, by considering the pore-throat, pore and grain distribution, and other macroscopic parameter

Flow unit characterization

3

2

1

(1 )Vgr T

ks K

1

(1 )Vgr T

k

s K

Kozeny-Carman equation:

Where:k = permeability, µm2

ø = porosity, fractionsVgr= specific surface area per unit grain volume, µm-1

KT = Kpsτ = effective zone factor

τ = tortuosity of the flow path

Divide with ø;

(1.1)

(1.2)

( )zRQI FZI

1e

ze

0.0314e

kRQI

Where:RQI = reservoir quality index

k = permeability, mDøe = effective porosity, fraction

• Flow Zone Indicator (FZI)

1

Vgr T

FZIs K

If the permeability is expressed in milidarcies and porosity as a fraction, the equation 1.2 becomes:


The flow zone indicator is defined from equation 1.1

Thus equation 1.2 can be written as:

(1.3)

(1.4)

Where, øz is the ratio of pore volume to grain volume;

(1.5)

Artificial Neural Network

1

N

i ii

y f w x

;

i

i

Where

x an input signal at i

y an output signal

f activation function

w weight or connection strength at i

a bias or threshold

Fig 11 Schematic of ANN model

WN

W2 f

x1

y

xN

x2

W1

An ANN is an information processing system that roughly replicates the behaviors of a human brain by emulating the operations and connectivity of biological neurons (Tsoukalas and Uhrig, 1997).

The use of Artificial Neural Network

• ANN is wildly used to solve problems such as classification, function approximation and pattern recognition. ( Lawrence, 1994, and Haykin ,1999)

• It is not suitable if the solution exists.

• ANN is good than other method (Master,1993) when;• Data is subject to large errors• The pattern data is deeply hidden• Data is unpredictable non-linearity

Back-propagation ANN (Rumelhart et al.,1986)

- Back-propagation Networks have a largest number of successful application especially for prediction.

- This method is a supervised learning technique for training multilayer neural network.

- The weights are adjusted during training to minimize error between known output and model output.

Application of ANN for well logging

• As the relationships of the well logging data and the reservoir properties are unknown, the neural network is proposed to predict the formation parameters (Helle et al., 2001).

• The advantage of this method are;• User does not need a deep geological knowledge of the

area.

• It is faster interpretation than conventional interpretation

Methodology

Data source : Gas fields, Malay basin, Gulf of Thailand

Data : 3 vertical well logging data (data in LAS format)

: 3 Routine Core Analysis (RCA) data : 3 Geological Data

Software : Interactive Petrophysics version3.3

( well logs interpretation)

: MATLAB version 6 with Neural Network Toolbox

( ANN model and simulation)

1. Data collection

Core interval

Well-1 Well-2 Well-3

Depth (ft) No. samples Depth (ft) No. samples Depth (ft) No. samples

1 3864.0- 3922.6 37 4016.0- 4070.0 41 3930.0--4021.0 69

2 3944.0-3971.9 21 4070.0-4155.3 84 4020.0- 4023.5 3

3 3971.0-3980.3 9 - - 4026.0- 4085.5 56

4 4094.0-4184.0 80 - - 4168.0-4256.0 87

5 4184.0-4271.7 76 - - 5139.0- 5222.0 81

6 - - - - 5560.4-5648.3 88

Total 223 Total 125 Total 384

Table 3 Number of core data available

Methodology1. Data collection

Collected data consisted of core analysis,i.e., permeability and porosity, and well logging data, i.e. GR,SP, DT, NPHI, RHOB and

RESD,RESM and RESS

Table 4 summary of well logging type run in Well-1,2 and 3 Well-1 Well-2 Well-3

Start (ft) 236.5 69.5 112.5

Stop (ft) 7880.5 9673 9824

Step (ft) 0.5 0.5 0.5

Log type

BS (in)

CALI (in)

DTCO (us/f)

DTSM (us/f)

GR (GAPI)

NPHI (v/v)

POTA (%)

RESD (ohm-m)

RESM (ohm-m)

RESS (ohm-m)

RHOB (g/cm3)

SP (mV)

THOR (ppm)

URAN (ppm)

Available for coring wells

Not available

Methodology

2. Well logging interpretation

Zoning

Sw determination

Rw determination

Porosity

Shale volume

Permeability

estimation

Lithology

Tools : GR, Caliper, Resistivity

Tools : Neutron and Density cross-plot was used to

identify formation lithology

Tools : GR

Shale Index = Vsh for linear trasformation

Tools : Density and Sonic

For shaly-sand, Correction is made by Vsh

Tools : Resistivity

Using Archies’equation

Tools: SP log (temperature and mud resistivity were known)

Permeability-porosity relationship from core analysis

Methodology


The flow unit characterization is studied from core analysis of each intervals. The following steps were done as follows:

- The RQI and øz are plotted in log-log scale- The flow zone indicator, FZI, was determined from the intercept where øz =1.- FZIs from each core intervals were used to determine the flow unit of the reservoir as the plots that lie on the same straight line have similar pore throat characteristics and constitute a flow unit. - Flow unit identification and construction well correlation.

In addition, the permeability-porosity relationships from core analysis can be plotted in semi-log scale. The slopes of the plot of each core interval are also used as supporting criteria for flow unit characterization. The permeability-porosity relationships of all core intervals each well were also used for well logging interpretation to determine the permeability from derived porosity.

ANN construction

Data case Well logging Case 1 Well logging Case 2 Well logging Case 3 Data setNumber of

sample% Testing

data

All well loggingand core available

DT,GR,NPHI,RESD,RHOB

DT,NPHI,RESD,RHOB

DT,GR,NPHI,RHOB

Training Well-1 179

-Well-2 100

TestingWell-1 44

19.83Well-2 25

Selected fromreservoir zone


DT,NPHI,RESD,RHOB

DT,GR,NPHI,RHOB

Training Well-1 139

-Well-2 52

TestingWell-1 34

19.41Well-2 12

Selected from flow unit


DT,NPHI,RESD,RHOB

DT,GR,NPHI,RHOB

Training Well-1 111

-Well-2 40

TestingWell-1 27

19.25Well-2 9

Table 5 Summary of ANN training and testing cases

Construction of ANNInput data : Selected well logging data (GR ,resistivity,RHOB, NPHI and DT)

Target output : Core analysis from a selected well Software : MATLAB Version 6.0

Design the ANN architecture

Training the ANN with training data set

Comparing the ANN models and selecting the optimal one

Testing the ANN model with a testing data set

Using the selected optimal model to predict the parameters

MethodologyANN model

Porosity

Permeability

RHOB

NPHI

•Input layer

GR

RESD

•Hidden layer

•Output layer

Sonic

W11,1

W1 S

1,R

W21,1

W2 s

2,s

1

Target output

Core analysis

Input data

(Training)

MethodologyANN model

Porosity

Permeability

RHOB

NPHI

•Input layer

GR

•Hidden layer

•Output layer

Sonic

W11,1

W1 S

1,R

W21,1

W2 s

2,s

1

ANN output

(predicted)

Input data

(Testing)

RESD

1. Well logging interpretation- Interpretation result of Well-1, 2 and 3

- Summary table of reservoir zones.

Results and Discussions


Fig 12 Well logging interpretation result of Well-1

Zone

Depth Thickness Permeability øe Sw Vcl

Top (ft) - Bottom (ft) (ft) (mD) (fraction) (fraction) (fraction)

2 3732.5 - 3770 37.5 1252.6 0.31 0.16 0.29

2 3784.5 - 3825.5 41 1407.65 0.26 0.32 0.34

2 3832 - 3860.5 28.5 352.99 0.24 0.28 0.2

2 3918 - 4007.5 89.5 159.75 0.25 0.23 0.1

3 4138 - 4209.5 71.5 102.51 0.25 0.18 0.07

3 4227 - 5042 815 328.39 0.24 0.36 0.22

3 5055 - 5541.5 486.5 34.84 0.21 0.16 0.05

3 5664 - 5694.5 30.5 44.49 0.23 0.15 0.01

3 5805.5 - 5893.5 88 25.56 0.2 0.37 0.19

Total 1688

Table 6 Summary of reservoir zone in Well-1

Zone



1 4010.5 - 4026.5 16 103.93 0.23 0.52 0.39

1 4030.5 - 4058 27.5 69.27 0.21 0.64 0.4

1 4064 - 4070 6 159.35 0.24 0.46 0.37

3 4102 - 4108 6 24.88 0.17 0.39 0.29

3 4118 - 4145.5 27.5 89.59 0.22 0.32 0.31

3 4186 - 4211.5 25.5 46.02 0.19 0.57 0.42

3 4303.5 - 4326 22.5 30.69 0.18 0.6 0.32

3 4337.5 - 4359.5 22 63.42 0.21 0.61 0.35

3 4398 - 4555.5 157.5 77.29 0.22 0.6 0.3

3 4568.5 - 4579.5 11 69.47 0.21 0.5 0.46

3 4603.5 - 4630.5 27 42.64 0.19 0.63 0.3

4 4639.5 - 4666 26.5 58.27 0.21 0.6 0.31

4 4673 - 4715.5 42.5 60.78 0.21 0.61 0.31

4 4725 - 4791 66 55.82 0.21 0.65 0.28

4 5009.5 - 5024.5 15 48.96 0.2 0.61 0.29

4 5057.5 - 5076 18.5 41.46 0.18 0.67 0.29

5 5119 - 5152 33 40.66 0.19 0.68 0.29

5 5179 - 5188.5 9.5 106.73 0.23 0.6 0.23

5 5350.5 - 5386.5 36 27.05 0.18 0.57 0.27

5 5438 - 5479.5 41.5 37.24 0.19 0.58 0.25

5 5954 - 5978.5 24.5 1681.57 0.26 0.28 0.27

Total 661.5


Zone



1 1952.5 - 1961.5 9 5258.25 0.35 0.35 0.23

1 2087.5 - 2104.5 17 16715.22 0.38 0.32 0.15

1 2113 - 2162 49 16338.15 0.37 0.35 0.18

1 2367.5 - 2377.5 10 19489.79 0.37 0.36 0.17

5 4187 - 4237.5 50.5 176.28 0.25 0.2 0.18

6 4259.5 - 4271.5 12 124.49 0.24 0.47 0.2

7 4474.5 - 4500.5 26 487.2 0.28 0.48 0.09

7 4563 - 4586 23 664.35 0.29 0.47 0.11

9 5132 - 5175 43 108.72 0.22 0.24 0.16

10 5263.5 - 5286 22.5 37.16 0.2 0.36 0.22

10 5320.5 - 5353 32.5 19462.07 0.27 0.22 0.22

11 5646 - 5683.5 37.5 33.13 0.2 0.43 0.24

12 5749 - 5792.5 43.5 863.88 0.22 0.17 0.15

13 6086 - 6099 13 48.07 0.19 0.37 0.17

13 6144 - 6178 34 109.85 0.22 0.29 0.11

13 6191.5 - 6200 8.5 259.51 0.26 0.3 0.04

13 6219 - 6231.5 12.5 13.13 0.18 0.35 0.14

Total 443.5


Results and Discussions2. Flow unit characterization

- Permeability and porosity relationship

- FZI determination

- Flow unit characterization

- Well correlation

0 10 20 30 40øe, %

0.1

1

10

100

1000

10000

Per

mea

bilit

y, m

D

C ore in te rva l 1D epth 3864.3-3922.5 ft






Fig 15 Porosity-permeability relationship of

all core interval in Well-1


2. Flow unit characterization (continued)


0 10 20 30 40øe, %

0.1

1

10

100

1000

10000

Per

mea

bilit

y, m

D

C ore in terval 1D epth 3864.3-3922.5 ft

Log(K)=0.650*Phi - 5 .717

0 10 20 30 40øe, %

0.1

1

10

100

1000

10000

Per

mea

bilit

y, m

D

C ore in terval 3D epth 3971.5-3979.5 ft

Log(K )=0.3669*Phi-0 .065

Fig 16 Porosity-permeability relationship of core interval No.1 in Well-1

Fig 17 Porosity-permeability relationship

of core interval No.3 in Well-1




0.1 1øz, fraction

0.01

0.1

1

10

RQ

I, m

C ore in terva l 1D epth 3864.3-3922.5ft





Fig 18 Flow unit characterization of all core interval in Well-1


- FZI determination


0.1 1øz, fraction

0.01

0.1

1

10

RQ

I, m


FZ I=4.596

Log(R Q I)=1 .842*Log(P h iz)+4.596

0.1 1øz, fraction

0.01

0.1

1

10

RQ

I, m

C ore in terva l 3D epth 3971.5-3979.5 ft

FZ I=1.146

Log(R Q I)=0.843*Log(Phiz)+1.146

Fig 19 Flow unit characterization of

core interval No.1 in Well-1 Fig 20 Flow unit characterization of core interval No.3 in Well-1


- FZI determination

Well Core interval Depth Slope Intercept

Well-1

1 3864.3 - 3922.5 0.65 -5.717

2 3945.2 - 3970 0.501 -1.678

3 3971.5 - 3979.5 0.367 -0.065

4 4094.3 - 4183.6 0.851 -11.28

5 4184.2 - 4271.6 0.574 -4.066

Well-2

1 4016.3 - 4069.3 0.388 -1.148

2 4070.5 - 4118.5 0.549 -2.154

3 4119.5 - 4154.5 0.565 -3.895

Well-3

1 3930.5 - 4020.5 0.577 -4.183

2 4020.5 - 4022.6 - -

3 4026.6 - 4085 0.954 -9.111

4 4168.5 - 4255.6 0.864 -11.052

5 5139.4 - 5220.6 0.87 -9.225

6 5560.4 - 5648.3 0.857 -9.408

Table 9 Summary of permeability and porosity relationship of each core interval


- Flow unit selection

Well Core interval Depth Slope FZI

Well-1

1 3864.3 - 3922.5 1.842 4.595

2 3945.2 - 3970 1.827 4.926

3 3971.5 - 3979.5 0.843 1.146

4 4094.3 - 4183.6 2.877 14.021

5 4184.2 - 4271.6 1.966 5.382

Well-2

1 4016.3 - 4069.3 0.7 1.244

2 4070.5 - 4118.5 1.542 4.35

3 4119.5 - 4154.5 1.089 1.779

Well-3

1 3930.5 - 4020.5 1.709 4.051

2 4020.5 - 4022.6 - -

3 4026.6 - 4085 2.089 9.392

4 4168.5 - 4255.6 2.707 13.602

5 5139.4 - 5220.6 2.378 13.122

6 5560.4 - 5648.3 2.048 7.403

Table 10 Summary of flow unit characterization of each core interval


Flow unit zone, FZI=13.-14.0

Flow unit zone, FZI =4.0-5.0

Well No.1

Well No.3

Well No.2

Flow unit zone, FZI =1.1-1.8

Fig 21 Flow unit correlation of Well-1, Well-2 and Well-3 with GR log


- Well correlation

Depth (Ft)

3500

4000

4500

5000

5500

5700

Core Well-1Core Well-2 Core Well-3

Interval 1Interval 2Interval 3

Interval 1Interval 3

Interval 4

Interval 5

Interval 6

Interval 1Interval 2Interval 3

Interval

Interval 4

Interval 5

FZI = 4.0-5.0

FZI = 1.1-1.8

FZI = 13.0-14.0

Unidentify flowUnit

Fig 22 Flow unit correlation of Well-1, Well-2 and Well-3 with core interval schematic


- Well correlation


- ANN porosity model

- Performance error

- Selected porosity models

- Regression analysis

- Test performance error

- Well-3 porosity prediction

3. ANN prediction model

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52M odel No.

0

20

40

60

Tes

ting

per

form

ance

err

orTra in w ith a ll data availab leCase 1 (D T,G R ,N PHI,RESD and R HO B)

Case 2 (D T,NPH I,R ESD and RH O B)Case 3 (D T,G R ,N PHI and RH O B)

Fig 23 Testing performance error of ANN porosity model from well log and core data available in case 1,2 and 3

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52M odel No.

0

20

40

60

Tes

ting

per

form

ance

err

or

Train w ith se lected reservoir dataCase 1 (D T,G R ,N PHI,RESD and R HO B)

Case 2 (D T,NPH I,R ESD and RH O B)Case 3 (D T,G R ,N PHI and RH O B)

Fig 24Testing performance error of ANN porosity model fromselected reservoir zone data in case 1,2 and 3

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52M odel No.

0

10

20

30

40

50

Tes

ting

per

form

ance

err

or

Train w ith se lected flow unit dataC ase 1 (D T,G R ,N PH I,R ESD and R H O B)

C ase 2 (D T,N PH I,R ESD and R H O B)C ase 3 (D T,G R ,N PH I and R H O B)

Fig 25 Testing performance error of ANN porosity model from selected flow unit data in case 1,2 and 3

- ANN Performance error

• All data from well logging and core analysis, 5 well logging input data, 14 hidden neurons and 5000 epochs, MSE= 23.40

• Selected data from reservoir zone, 4 well logging input data (without GR), 15 hidden neurons and 8000 epochs, MSE= 15.66

• Selected data from flow unit, 4 well logging input data (without GR), 17 hidden neurons and 500 epochs, MSE= 18.85

- Selected porosity models

Fig 26 Regression analysis of a selected ANN porosity model

10 15 20 25 30 350

5

10

15

20

25

30

35

Porosity from core measurement, %

Por

osity

fro

m A

NN

ana

lysi

s, %

R = 0.685

Fig 27 Performance test of a selected ANN porosity model

0 5 10 15 20 25 30 35 40 45 50

3850

3900

3950

4000

4050

4100

4150

4200

4250

4300

Predicted porosity of testing data, %

Dep

th,

ft

ANN outputCore analysis

Fig 28 Regression analysis of predicted ANN porosityversus core data of Well-3

10 12 14 16 18 20 22 24 26 28 3010

15

20

25

30

35

Porosity from core measurement, %

Por

osity

fro

m A

NN

ana

lysi

s, %

Fig 29 The result of ANN porosity model from selected reservoir zone data in case 1,2 and 3 Depth 6088 – 6325 ft

- Well-3 porosity prediction result


ANN permeability model

- Performance error

- Selected permeability models

- Regression analysis

- Test performance error

- Well-3 permeability prediction result

3. ANN prediction model (continued)

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52M ode l N o.

0

40000

80000

120000

Te

stin

g p

erfo

rman

ce e

rror

Tra in w ith a ll data ava ilab leC ase 1 (D T,G R ,N P H I,R E SD and R H O B )

C ase 2 (D T,N PH I,R ES D and R H O B)

C ase 3 (D T,G R ,N P H I and R H O B)

Fig 30 Testing performance error of ANN permeability modelfrom well logging and core data available in case 1,2 and 3

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52M odel N o.

0

20000

40000

60000

80000

100000

Te

stin

g p

erfo

rman

ce e

rror

Train w ith selected reservoir dataCase 1 (DT,G R,NPHI,RESD and RHO B)

Case 2 (DT,NPHI,RESD and RHOB)Case 3 (DT,G R,NPHI and RHOB)

Fig 31 Testing performance error of ANN permeability model from reservoir zone data in case 1,2 and 3

Fig 32 Testing performance error of ANN permeability model from flow unit data in case 1,2 and 3

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52M odel N o.

0

4000

8000

12000

16000

20000

Tes

ting

per

form

ance

err

or

Train w ith se lected flow unit dataCase 1 (D T,G R ,N PHI,RESD and R HO B)

Case 2 (D T,NPH I,R ESD and RH O B)Case 3 (D T,G R ,N PHI and R H O B)

- ANN performance error

• All data from well logging and core analysis, 4 well logging input node (without GR), 15 hidden neuron and 8000 epochs, MSE= 39,169

• Selected data from reservoir zone, 4 well logging input node (without GR), 16 hidden neurons and 8000 epochs, MSE= 27,513

• Selected data from flow unit, 4 well logging input node (without GR), 9 hidden neurons and 8000 epochs, MSE= 11,736

- Selected permeability models


Fig 33 Performance test of a selected ANN permeability model

-50 0 50 100 150 200 250 300-200

0

200

400

600

800

1000

Permeability from core measurement, mD

Per

mea

bilit

y fr

om A

NN

ana

lysi

s, m

DR = 0.407

Fig 34 Performance test of a selected ANN porosity model

10-1

100

101

102

103

104

3850

3900

3950

4000

4050

4100

4150

4200

Tested permeability by ANN, mD

Dep

th,

ft

ANN outputCore analysis

Fig 35 Regression analysis of predicted ANN permeabilityversus core data of Well-3

10-2

10-1

100

101

102

103

10-2

10-1

100

101

102

103

104

Permeability from core measurement, mD

Per

mea

bilit

y fr

om A

NN

ana

lysi

s, m

D

Fig 36 The result of ANN permeability model from selected flow unit data in case 1,2 and 3 Depth 5975-6230 ft

- Well-3 permeability prediction result

Conclusions and Recommendations

• Conclusion

– An Integrated well logging interpretation was done for three wells in the North Malay basin to evaluate porosity, permeability and water saturation.

– The flow unit characterization gives a better understanding of hydraulic flow pattern. The same flow unit might have consistent petrophysical and fluid properties. The flow units were characterized using FZI and the permeability and porosity relationships in this study, three flow units were observed, having FZI equal to 1.5, 4.5 and 13.8.

– The ANN for permeability and porosity prediction was constructed and

used to predict permeability and porosity from well logging in the study area. The ANN and permeability porosity models has the least performance error when input data are selected only from reservoir zone with four input data of DT, NPHI, RESD and RHOB.

Conclusions and Recommendations

• Conclusion (Continued)

– The ANN for porosity and permeability prediction for this study area were found as follows:

(i) ANN porosity model: selected data from reservoir zone, 4 well logging input node (without GR), 15 hidden neurons and 8000 epochs, MSE= 15.66

(ii) ANN permeability model : Selected data from flow unit, 4 well logging input node (without GR), 9 hidden neurons and 8000 epochs, MSE= 11,736

thesis topic an integrated petrophysical study using well logging data for evaluation of a gas field...

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