developing synthetic well logs for the upper …shahab.pe.wvu.edu/publications/pdfs/spe98013.pdf ·...

RolonRolon, L. F., , L. F., MohagheghMohaghegh, S. D., , S. D., AmeriAmeri, S. and , S. and GaskariGaskari, R. , R.

West Virginia UniversityWest Virginia UniversityMcDaniel, B. A. McDaniel, B. A.

Dominion E&PDominion E&P

Morgantown, September 16th 2005

SPE 98013SPE 98013

Developing Synthetic Well Logs for Developing Synthetic Well Logs for the Upper Devonian Units in the Upper Devonian Units in

Southern PennsylvaniaSouthern Pennsylvania

ObjectiveObjective

To develop a methodology to generate synthetic wireline logs using an Artificial Neural Network in conjunction with data from conventional wireline logs.

Synthetic logs can help analyze the reservoir properties in areas where the set of logs that are necessary, are absent or incomplete .

OutlineOutline

• Introduction• Location• Geology

• Methodology• Results• Conclusions

LocationLocation

Southwestern Pennsylvania, Southwestern Pennsylvania, Armstrong Co.Armstrong Co.

Armstrong Co.Armstrong Co.

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CrossCross--Section A Section A –– A’A’

StratigraphyStratigraphy

Up

perm

ost

Devo

nia

n

362 m.y.

367 m.y.

Cat

skill

Del

ta Ven

ango P

lay

Bra

dfo

rd

Play

2nd Bradford2nd Bradford

SpeechleySpeechley

GordonGordon

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MurrysvilleMurrysville

CrossCross--section Lower Zonesection Lower Zone

SW (A) NE (A’)

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StructureStructure

Murrysville Anticline

OutlineOutline



• The neural network model to create synthetic logs was developed in NeuroShell®2.

• The algorithm used to build the model was General Regression

• The architecture used consisted of three layers: • input layer - 7 neurons• hidden layer - 7000 neurons• output layer - 1 neuron

MethodologyMethodology

DATA MATRIXDATA MATRIX

ID DEPTH LAT LONG RILD DEN NPRL GRGC DNND157 2000 40.5859 79.4719 32.87 2.7 8.79 144.66 15775.31157 2000 40.5859 79.4719 31.73 2.71 9.08 145.1 15718.19157 1999 40.5859 79.4719 30.91 2.71 9.38 142.85 15628.33157 1999 40.5859 79.4719 30.82 2.71 9.58 141.16 15647.24157 1998 40.5859 79.4719 31.57 2.71 9.61 142.1 15765.67157 1998 40.5859 79.4719 32.53 2.69 9.43 142.63 15928.85

ID DEPTH LAT LONG RILD DEN NPRL GRGC DNND157 2000 40.5859 79.4719 32.87 2.7 8.79 144.66 15775.31157 2000 40.5859 79.4719 31.73 2.71 9.08 145.1 15718.19157 1999 40.5859 79.4719 30.91 2.71 9.38 142.85 15628.33157 1999 40.5859 79.4719 30.82 2.71 9.58 141.16 15647.24157 1998 40.5859 79.4719 31.57 2.71 9.61 142.1 15765.67157 1998 40.5859 79.4719 32.53 2.69 9.43 142.63 15928.85

XYZ RESISTIVITY

DENSIITY GAMMA RAY

NEUTRON

Combination of Inputs/OutputsCombination of Inputs/Outputs

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DEN GRRE S NEU XYZ

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Y

Z

I I I

DEN GRRES NEU XYZ

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Y

Z

IA I I I

I A

DEN GRRE S NEU XYZ

X

Y

Z

I I I

A

I

= Actual Output

= Inputs

RES = Resistivity

DEN = Density

GR = Gamma Ray

NEU = Neutron

XYZ = Coordinates and Depths

Combination A

Combination B

Combination C

• Methodology carried out through two exercises:

– Exercise 1: Four wells combined.

– Exercise 2: Three wells combined, one out.

MethodologyMethodology

11STST EXERCISE EXERCISE -- Four Wells CombinedFour Wells Combined

• Four wells were used for development and training of the network

Training and

testingwells

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168

169

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Verification wells

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• Then each one of these wells was used for verification of the trained network.

Pro

duct

ion

Set

Training and

calibrationwells

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Verificationwell

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Training and

calibrationwells

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Verificationwell

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calibrationwells

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Verificationwell

Training and

calibrationwells

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169

22NDND EXERCISE EXERCISE -- Three wells combined, one outThree wells combined, one out

• Three wells were used for development and training of the network.

• A fourth well, never used during training and calibration, was selected for verification of the network.

First Attempt First Attempt -- Buffalo Valley FieldBuffalo Valley Field

NEW MEXICO

TEXAS

OKLAHOMA

MEXICO

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1 mile

219219 754754665665

OutlineOutline



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Gamma Ray (API units)

Well 219Well 219Buffalo Valley FieldBuffalo Valley Field

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Resistivity (ohm-m)


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Data Set R2 Data Set R2 Data Set R2

TRN 0.9555 TRN 0.9715 TRN 0.8664TST 0.9377 TST 0.9603 TST 0.8254

PRO well 754 -0.4601 PRO well 754 -0.1129 PRO well 754 -0.1082



PRO well 665 0.3422 PRO well 665 -1.4643 PRO well 665 0.2685







COMBINATION A COMBINATION B COMBINATION CTraining wells: 219, 321, 665 Training wells: 219, 321, 665 Training wells: 219, 321, 665Verification well: 754 Verification well: 754 Verification well: 754

Training wells: 219, 321, 754 Training wells: 219, 321, 754 Training wells: 219, 321, 754Verification well: 665 Verification well: 665 Verification well: 665



Exercise 2 Exercise 2 -- Buffalo Valley FieldBuffalo Valley Field

Verification data set well 219

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Verification data set well 219

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Southern Pennsylvania AreaSouthern Pennsylvania Area

Upper zoneUpper zoneGordonGordon

100 Foot100 Foot

MurrysvilleMurrysville

Exercise 1Exercise 1-- Upper zoneUpper zone

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Data Set R2

TRN 0.9377TST 0.9412PRO 0.9207

PRO well 157 0.9264PRO well 168 0.9619PRO well 169 0.9102PRO well 174 0.9262

Data Set R2

TRN 0.8338TST 0.8225PRO 0.8099


Data Set R2

TRN 0.942TST 0.9291PRO 0.9234


Outputs: Neutron

Inputs: Resistivity, Gamma Ray, Neutron, XYZOutputs: Density

COMBINATION CInputs: Resistivity, Density, Gamma Ray, XYZ

COMBINATION AInputs: Density, Gamma Ray, Neutron, XYZOutputs: Resistivity

COMBINATION B

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Exercise 2Exercise 2 -- Upper zone Upper zone

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PRO well 174 0.9091 PRO well 174 0.6002 PRO well 174 0.7854










Verification well: 157 Verification well: 157 Verification well: 157


Training wells: 174, 169, 168 Training wells: 174, 169, 168 Training wells: 174, 169, 168





Training wells: 157, 168, 169 Training wells: 157, 168, 169 Training wells: 157, 168, 169COMBINATION A COMBINATION B COMBINATION C

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Lower zoneLower zone

2nd Bradford2nd Bradford

SpeechleySpeechley

Exercise 1Exercise 1-- Upper zoneUpper zone

Data Set R2

TRN 0.9536TST 0.9388PRO 0.9426


Data Set R2

TRN 0.8118TST 0.7946PRO 0.8336


Data Set R2

TRN 0.9313TST 0.9133PRO 0.9311


Outputs: Neutron

Inputs: Resistivity, Gamma Ray, Neutron, XYZOutputs: Density

COMBINATION CInputs: Resistivity, Density, Gamma Ray, XYZ

COMBINATION AInputs: Density, Gamma Ray, Neutron, XYZOutputs: Resistivity

COMBINATION B

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Exercise 2 Exercise 2 -- Upper zoneUpper zone

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Verification well: 157

Combination ATraining wells: 157, 168, 169Verification well: 174

Verification well: 169

Verification well: 174 Verification well: 174


Combination B Combination CTraining wells: 157, 168, 169 Training wells: 157, 168, 169



Training wells: 157, 174, 169 Training wells: 157, 174, 169Training wells: 157, 174, 169Verification well: 168



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Exercise 1 - Upper Zone (1000' to 2000')

0.00.10.20.30.40.50.60.70.80.91.0

TRN TST PRO PROwell 157

PROwell 168

PROwell 169

PROwell 174

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quar

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Combinacion C

Combinations of inputs and outputs Combinations of inputs and outputs

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TRN TST PRO PROwell157

PROwell168

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Combination B

Combination C


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0.00000.10000.20000.30000.40000.50000.60000.70000.80000.90001.0000

PRO well 157 PRO well 168 PRO well 169 PRO well 174

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Combination C


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Well 157Well 168Well 169Well 174

Well Location Well Location

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0.00000.10000.20000.30000.40000.50000.60000.70000.80000.90001.0000

PRO well 157 PRO well 168 PRO well 169 PRO well 174

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Combination B

Combination C


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Well 157Well 168Well 169Well 174

Well Location Well Location

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OutlineOutline



Conclusions Conclusions

• Synthetic logs with a reasonable degree of accuracy were generated through the approach before described.

• Best performance was obtained for combination A of inputs and outputs, then for combination C, and finally for combination B.

• Accuracy of synthetic logs may be favored by interpolation of data.

• Quality of data plays a very important role in developing of a neural network model.

Conclusions Conclusions

• A recommendation for future works is to do a very careful quality control of the data before a neural network model is build.

• Lithologic heterogeneities in the reservoir do not affect significantly performance of a neural network model in generation of synthetic logs.

developing synthetic well logs for the upper …shahab.pe.wvu.edu/publications/pdfs/spe98013.pdf ·...

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