Machine Learning for Subsurface Characterization

442 Pages·2019·86.449 MB·English

by  Siddharth Misra, Hao Li, Jiabo He

Machine Learning for Subsurface
Characterization
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Machine Learning
for Subsurface
Characterization
Siddharth Misra
Hao Li
Jiabo He
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Dedication
This book is dedicated to our parents: Rabindra,
Surekha, Tao, Jianying, Weidong, and Beihong.
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Contents
Contributors xvii
Preface xix
Acknowledgment xxvii
1. Unsupervised outlier detection techniques for well
logs and geophysical data 1
SiddharthMisra,OghenekaroOsogbaandMarkPowers
1. Introduction 2
1.1. Basicterminologiesinmachinelearninganddata-driven
models 3
1.2. Typesofmachinelearningtechniques 3
1.3. Typesofoutliers 4
2. Outlierdetectiontechniques 5
3. Unsupervisedoutlierdetectiontechniques 7
3.1. Isolationforest 8
3.2. One-classSVM 8
3.3. DBSCAN 10
3.4. Localoutlierfactor 11
3.5. Influenceofhyperparametersontheunsupervised
ODTs 12
4. Comparativestudyofunsupervisedoutlierdetection
methodsonwelllogs 14
4.1. Descriptionofthedatasetusedforthecomparativestudy
ofunsupervisedODTs 15
4.2. Datapreprocessing 15
4.3. Validationdataset 17
4.4. Metrics/scoresfortheassessmentoftheperformances
ofunsupervisedODTsontheconventionallogs 21
5. PerformanceofunsupervisedODTsonthefourvalidation
datasets 26
5.1. PerformanceonDataset#1containingnoisy
measurements 26
5.2. PerformanceonDataset#2containingmeasurements
affectedbybadholes 28
5.3. PerformanceonDataset#3containingshalylayers
andbadholeswithnoisymeasurements 29
vii
viii Contents
5.4. PerformanceonDataset#4containingmanuallylabeled
outliers 30
6. Conclusions 32
AppendixA. Popularmethodsforoutlierdetection 33
AppendixB. Confusionmatrixtoquantifytheinlier
andoutlierdetectionsbytheunsupervised
ODTs 34
AppendixC. Valuesofimportanthyperparametersofthe
unsupervisedODTmodels 34
AppendixD. Receiveroperatingcharacteristics(ROC)and
precision-recall(PR)curvesforvarious
unsupervisedODTsontheDataset#1 34
Acknowledgments 36
References 36
2. Unsupervised clustering methods for noninvasive
characterization of fracture-induced geomechanical
alterations 39
SiddharthMisra,AdityaChakravarty,PriteshBhoumickand
ChandraS.Rai
1. Introduction 40
2. Objectiveofthisstudy 41
3. Laboratorysetupandmeasurements 41
4. Clusteringmethodsfortheproposednoninvasive
visualizationofgeomechanicalalterations 44
4.1. K-meansclustering 44
4.2. Agglomerativeclustering 45
4.3. DBSCAN 47
5. Features/attributesfortheproposednoninvasive
visualizationofgeomechanicalalteration 48
5.1. Featureengineering 49
5.2. Dimensionalityreduction 52
6. Resultsanddiscussions 53
6.1. Effectoffeatureengineering 53
6.2. Effectofclusteringmethod 55
6.3. Effectofdimensionalityreduction 57
6.4. Effectofusingfeaturesderivedfrombothprefracture
andpostfracturewaveforms 58
7. Physicalbasisofthefracture-inducedgeomechanical
alterationindex 59
8. Conclusions 61
Acknowledgments 62
References 62
Contents ix
3. Shallow neural networks and classification methods
for approximating the subsurface in situ fluid-filled
pore size distribution 65
SiddharthMisraandJiaboHe
1. Introduction 66
2. Methodology 67
2.1. Hydrocarbon-bearingshalesystem 67
2.2. Petrophysicalbasisfortheproposeddata-drivenlog
synthesis 68
2.3. Datapreparationandstatisticalinformation 69
2.4. Categorizationofdepthsusingflags 72
2.5. FittingtheT distributionwithabimodalGaussian
2
distribution 74
2.6. Min-maxscalingofthedataset(featuresandtarget) 77
2.7. TrainingandtestingmethodologyfortheANNmodels 78
3. ANNmodeltraining,testing,anddeployment 81
3.1. ANNmodels 81
3.2. TrainingthefirstANNmodel 81
3.3. TestingthefirstANNmodel 84
3.4. TrainingthesecondANNmodel 84
3.5. TestingthesecondANNmodel 86
3.6. PetrophysicalvalidationofthefirstANNmodel 86
3.7. ANN-basedpredictionsofNMRT distributionforvarious
2
depthintervals 87
4. Conclusions 89
AppendixA. Statisticalpropertiesofconventionallogsand
inversion-derivedlogsforvariousdepth
intervals 89
AppendixB. Categorizationofdepthsusingflags(categorical
features) 96
AppendixC. Importanceofthe12conventionallogsand
10inversion-derivedlogs 97
AppendixD. Estimationsofspecificreservoirparametersfrom
NMRT distributions 98
2
References 100
4. Stacked neural network architecture to model the
multifrequencyconductivity/permittivity responses
of subsurface shale formations 103
SiddharthMisraandJiaboHe
1. Introduction 103
2. Method 106
2.1. Datapreparation 106
2.2. Methodologyforthedielectricdispersion(DD)log
synthesis 106