Unconventional Hydrocarbon Resources

Sid-Ali Ouadfeul, Professor, Department of Geophysics, Geology and Reservoir Engineering, Algerian Petroleum Institute-IAP Corporate University, Algeria.

Preface xiii

1 Predrill Pore Pressure Estimation in Shale Gas Reservoirs Using Seismic Genetic Inversion with an Example from the Barnett Shale 1
Sid-Ali Ouadfeul, Mohamed Zinelabidine Doghmane, and Leila Aliouane

1.1 Introduction 1

1.2 Methods and Application to Barnett Shale 2

1.2.1 Geological Setting 2

1.2.2 Methods 3

1.3 Data Processing 6

1.4 Results Interpretation and Conclusions 7

References 9

2 An Analysis of the Barnett Shale’s Seismic Anisotropy’s Role in the Exploration of Shale Gas Reservoirs (United States) 11
Sid-Ali Ouadfeul, Leila Aliouane, Mohamed Zinelabidine Doghmane, and Amar Boudella

2.1 Introduction 11

2.2 Seismic Anisotropy 12

2.3 Application to Barnett Shale 14

2.3.1 Geological Setting 14

2.3.2 Data Analysis 15

2.4 Conclusions 18

References 18

3 Wellbore Stability in Shale Gas Reservoirs with a Case Study from the Barnett Shale 21
Sid-Ali Ouadfeul, Mohamed Zinelabidine Doghmane, and Leila Aliouane

3.1 Introduction 21

3.2 Wellbore Stability 22

3.2.1 Mechanical Stress 22

3.2.2 Chemical Interactions with the Drilling Fluid 22

3.2.3 Physical Interactions with the Drilling Fluid 22

3.3 Pore Pressure Estimation Using the Eaton’s Model 23

3.4 Shale Play Geomechanics and Wellbore Stability 24

3.5 Application to Barnett Shale 26

3.5.1 Geological Context 26

3.5.2 Data Processing 28

3.6 Conclusion 28

References 30

4 A Comparison of the Levenberg-Marquardt and Conjugate Gradient Learning Methods for Total Organic Carbon Prediction in the Barnett Shale Gas Reservoir 31
Sid-Ali Ouadfeul, Mohamed Zinelabidine Doghmane, and Leila Aliouane

4.1 Introduction 31

4.2 Levenberg-Marquardt Learning Algorithm 32

4.3 Application to Barnett Shale 33

4.3.1 Geological Setting 33

4.3.2 Data Processing 33

4.3.3 Results Interpretation 36

4.4 Conclusions 39

References 40

5 Identifying Sweet Spots in Shale Reservoirs 41
Susan Smith Nash

5.1 Introduction 41

5.2 Materials and Methods 41

5.3 Data for Two Distinct Types of Sweet Spot Identification Workflows 42

5.3.1 Workflow 5.1: Early-Phase Workflow Elements: Total Petroleum System Approach 42

5.3.2 Workflow 5.2: Smaller-Scale Field-Level Tools and Techniques 43

5.4 Results: Two Integrative Workflows 45

5.4.1 Early-Phase Exploration Workflow 45

5.4.2 Later Phase Developmental, Including Refracing Workflow 45

5.5 Case Studies 46

5.5.1 Woodford Shale: Emphasis on Chemostratigraphy 46

5.5.2 Barnett Shale: Emphasis on Seismic Attributes 46

5.5.3 Eagle Ford Shale: Pattern Recognition/Deep Learning 47

5.6 Conclusion 47

References 47

6 Surfactants in Shale Reservoirs 49
Susan Smith Nash

6.1 Introduction 49

6.2 Function of Surfactants 49

6.2.1 Drilling 50

6.2.2 Completion (Hydraulic Fracturing) 50

6.3 Materials and Methods 50

6.4 Characteristics of Shale Reservoirs 50

6.4.1 High Clay Mineral Content 51

6.4.2 Nano-Sized Pores 51

6.4.3 Mixed-Wettability Behavior 51

6.4.4 High Capillary Pressures 51

6.5 The Klinkenberg Correction 51

6.5.1 Klinkenberg Gas Slippage Measurement 52

6.6 Completion Chemicals to Consider in Addition to the Surfactant 52

6.6.1 Enhanced Oil Recovery (EOR) 52

6.6.2 Liquids-Rich Shale Plays After Initial Decline 53

6.7 Mono-Coating Proppant 53

6.7.1 Zwitterionic Coating 53

6.8 Dual-Coating Proppant 54

6.8.1 Outside Coating 54

6.8.2 Inner Coating 54

6.9 Dual Coating with Porous Proppant 54

6.9.1 Zwitterionic Outer Coating; Inorganic Salt Inner Coating, Porous Core 54

6.10 Data 55

6.10.1 Types of Surfactants 55

6.10.1.1 Anionic 55

6.10.1.2 Cationic 56

6.10.1.3 Nonionic 56

6.10.1.4 Zwitterionic 56

6.11 Examples of Surfactants in Shale Plays 56

6.11.1 Bakken (Wang and Xu 2012) 56

6.11.2 Eagle Ford (He and Xu 2017) 57

6.11.3 Utica (Shuler et al. 2016) 57

6.12 Results 57

6.13 Shale Reservoirs, Gas, and Adsorption 57

6.14 Operational Conditions 58

6.15 Conclusions 59

References 59

7 Neuro-Fuzzy Algorithm Classification of Ordovician Tight Reservoir Facies in Algeria 61
Mohamed Zinelabidine Doghmane, Sid-Ali Ouadfeul, and Leila Aliouane

7.1 Introduction 61

7.2 Neuro-Fuzzy Classification 61

7.3 Results Discussion 63

7.4 Conclusion 67

References 67

8 Recognition of Lithology Automatically Utilizing a New Artificial Neural Network Algorithm 69
Mohamed Zinelabidine Doghmane, Sid-Ali Ouadfeul, and Leila Aliouane

8.1 Introduction 69

8.2 Well-Logging Methods 70

8.2.1 Nuclear Well Logging 70

8.2.2 Neutron Well Logging 70

8.2.3 Sonic Well Logging 70

8.3 Use of ANN in the Oil Industry 71

8.4 Lithofacies Recognition 71

8.5 Log Interpretation 72

8.5.1 Methodology of Manual Interpretation 72

8.5.2 Results of Manual/Automatic Interpretation 73

8.6 Conclusion 78

References 79

9 Construction of a New Model (ANNSVM) Compensator for the Low Resistivity Phenomena Saturation Computation Based on Logging Curves 81
Mohamed Zinelabidine Doghmane, Sid-Ali Ouadfeul, and Leila Aliouane

9.1 Introduction 81

9.2 Field Geological Description 82

9.2.1 Conventional Interpretation 82

9.2.2 Reservoir Mineralogy 84

9.3 Low-Resistivity Phenomenon 84

9.3.1 Cross Plots Interpretation 84

9.3.2 NMR Logs Interpretation 85

9.3.3 Comparison Between Well-1 and Well- 2 85

9.3.4 Developed Logging Tools 85

9.3.5 Proposed ANNSVM Algorithm 85

9.4 Conclusions 91

References 91

10 A Practical Workflow for Improving the Correlation of Sub-Seismic Geological Structures and Natural Fractures using Seismic Attributes 93
Mohamed Zinelabidine Doghmane, Sid-Ali Ouadfeul, and Leila Aliouane

10.1 Introduction 93

10.2 Description of the Developed Workflow 94

10.3 Discussion 94

10.4 Conclusions 95

References 96

11 Calculation of Petrophysical Parameter Curves for Nonconventional Reservoir Modeling and Characterization 99
Mohamed Zinelabidine Doghmane, Sid-Ali Ouadfeul, and Leila Aliouane

11.1 Introduction 99

11.2 Proposed Methods 99

11.3 Results and Discussion 101

11.4 Conclusions 101

References 102

12 Fuzzy Logic for Predicting Pore Pressure in Shale Gas Reservoirs With a Barnett Shale Application 105
Leila Aliouane, Sid-Ali Ouadfeul, Mohamed Zinelabidine Doghmane, and Amar Boudella

12.1 Introduction 105

12.2 The Fuzzy Logic 106

12.3 Application to Barnett Shale 106

12.3.1 Geological Context 106

12.3.2 Data Processing 107

12.4 Results Interpretation and Conclusions 110

References 111

13 Using Well-Log Data, a Hidden Weight Optimization Method Neural Network Can Classify the Lithofacies of a Shale Gas Reservoir: Barnett Shale Application 113
Leila Aliouane, Sid-Ali Ouadfeul, Mohamed Z. Doghmane, and Ammar Boudella

13.1 Introduction 113

13.2 Artificial Neural Network 114

13.3 Hidden Weight Optimization Algorithm Neural 114

13.4 Geological Context of the Barnett Shale 115

13.5 Results Interpretation and Conclusions 117

Bibliography 124

14 The Use of Pore Effective Compressibility for Quantitative Evaluation of Low Resistive Pays 127
Mohamed Zinelabidine Doghmane, Sid-Ali Ouadfeul, and Leila Aliouane

14.1 Introduction 127

14.2 Low-Resistivity Pays in the Studied Basin 127

14.3 Water Saturation from Effective Pore Compressibility 128

14.4 Discussion 128

14.5 Conclusions 130

Bibliography 130

15 The Influence of Pore Levels on Reservoir Quality Based on Rock Typing: A Case Study of Quartzite El Hamra, Algeria 133
Nettari Ferhat, Mohamed Z. Doghmane, Sid-Ali Ouadfeul, and Leila Aliouane

15.1 Introduction 133

15.2 Quick Scan Method 133

15.3 Results 135

15.4 Discussion 135

15.5 Conclusions 137

Bibliography 137

16 An Example from the Algerian Sahara Illustrates the Use of the Hydraulic Flow Unit Technique to Discriminate Fluid Flow Routes in Confined Sand Reservoirs 139
Abdellah Sokhal, Sid-Ali Ouadfeul, Mohamed Zinelabidine Doghmane, and Leila Aliouane

16.1 Introduction 139

16.2 Regional Geologic Setting 140

16.3 Statement of the Problem 142

16.3.1 Concept of HFU 142

16.3.2 HFU Zonation Process 142

16.4 Results and Discussion 143

16.4.1 FZI Method 143

16.4.2 FZI Method 144

16.5 Conclusions 146

References 146

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17 Integration of Rock Types and Hydraulic Flow Units for Reservoir Characterization. Application to Three Forks Formation, Williston Basin, North Dakota, USA 147
Aldjia Boualam and Sofiane Djezzar

17.1 Introduction 147

17.2 Petrophysical Rock-Type Prediction 148

17.3

Rock Types’ Classification Based on R 35 Pore Throat Radius 150

17.3.1 Upper Three Forks 153

17.3.2 Middle Three Forks 155

17.3.3 Lower Three Forks 157

17.4 Determination of Hydraulic Flow Units 157

17.4.1 Upper Three Forks 159

17.4.2 Middle Three Forks 160

17.4.3 Lower Three Forks 160

17.5 Conclusion 160

References 162

18 Stress-Dependent Permeability and Porosity and Hysteresis. Application to the Three Forks Formation, Williston Basin, North Dakota, USA 163
Aldjia Boualam and Sofiane Djezzar

18.1 Introduction 163

18.2 Database 165

18.3 Testing Procedure 166

18.3.1 Core Samples Cleaning and Drying 167

18.3.2 Permeability and Porosity Measurements 169

18.3.3 Mineral Composition Analysis 170

18.3.4 Scanning Electron Microscope 171

18.4 Results and Discussions 174

18.4.1 Stress-Dependent Permeability and Hysteresis 175

18.4.1.1 Upper Three Forks 175

18.4.1.2 Middle Three Forks 181

18.4.2 Permeability Evolution with Net Stress 183

18.4.3 Stress-Dependent Porosity and Hysteresis 186

18.4.3.1 Upper Three Forks 186

18.4.3.2 Middle Three Forks 192

18.4.4 Porosity Evolution with Net Stress 194

18.4.5 Permeability Evolution with Porosity 195

18.5 Conclusion 196

References 198

19 Petrophysical Analysis of Three Forks Formation in Williston Basin, North Dakota, USA 207
Aldjia Boualam and Sofiane Djezzar

19.1 Introduction 207

19.2 Petrophysical Database 208

19.2.1 Curve Editing and Environmental Correction 209

19.2.2 Preanalysis Processing 211

19.3 Methods and Background 211

19.3.1 Wireline Logs 211

19.3.1.1 Caliper Tool 211

19.3.1.2 Total and Spectral Gamma-Ray Logs 212

19.3.1.3 Electrical Properties (Resistivity) 212

19.3.1.4 Neutron Logs 213

19.3.1.5 Bulk Density Log 213

19.3.1.6 Acoustic Logs 213

19.3.1.7 Elemental Capture Spectroscopy 214

19.3.1.8 Nuclear Magnetic Resonance 215

19.3.1.9 Multifrequency Array Dielectric Measurements 215

19.3.2 Petrophysical Analysis Challenges 216

19.3.2.1 Formation Components and Volumes 217

19.3.2.2 Water Saturation Model 221

19.3.2.3 Nuclear Magnetic Resonance 224

19.4 Petrophysical Analysis Results and Discussion 224

19.4.1 Upper Three Forks 231

19.4.2 Middle Three Forks 236

19.4.3 Lower Three Forks 237

19.5 Conclusion 240

References 241

20 Water Saturation Prediction Using Machine Learning and Deep Learning. Application to Three Forks Formation in Williston Basin, North Dakota, USA 251
Aldjia Boualam and Sofiane Djezzar

20.1 Introduction 251

20.2 Experimental Procedure and Methodology 253

20.2.1 Support Vector Machine Concepts 253

20.2.2 Preprocessing of the Dataset 255

20.2.3 Building SVR Model 258

20.2.4 Building Random Forest Regression Model 261

20.2.5 Building Deep Learning Model 262

20.2.6 Curve Reconstruction Using K.Mod 264

20.3 Results and Discussion 264

20.4 Conclusion 275

References 276

Appendix Hysteresis Testing and Mineralogy 285

Index 297