About the Book
Unconventional Hydrocarbon Resources Enables readers to save time and effort in exploring and exploiting shale gas and other unconventional fossil fuels by making use of advanced predictive tools
Unconventional Hydrocarbon Resources highlights novel concepts and techniques for the geophysical exploration of shale and other tight hydrocarbon reservoirs, focusing on artificial intelligence approaches for modeling and predicting key reservoir properties such as pore pressure, water saturation, and wellbore stability. Numerous application examples and case studies present real-life data from different unconventional hydrocarbon fields such as the Barnett Shale (USA), the Williston Basin (USA), and the Berkine Basin (Algeria).
Unconventional Hydrocarbon Resources explores a wide range of reservoir properties, including modeling of the geomechanics of shale gas reservoirs, petrophysics analysis of shale and tight sand gas reservoirs, and prediction of hydraulic fracturing effects, fluid flow, and permeability.
Sample topics covered in Unconventional Hydrocarbon Resources include:
Calculation of petrophysical parameter curves for non-conventional reservoir modeling and characterization
Comparison of the Levenberg-Marquardt and conjugate gradient learning methods for total organic carbon prediction in the Barnett shale gas reservoir
Use of pore effective compressibility for quantitative evaluation of low resistive pays and identifying sweet spots in shale reservoirs
Pre-drill pore pressure estimation in shale gas reservoirs using seismic genetic inversion
Using well-log data to classify lithofacies of a shale gas reservoir
Unconventional Hydrocarbon Resources is a valuable resource for researchers and professionals working on unconventional hydrocarbon exploration and in geoengineering projects.
Table of Contents:
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
0005546230.indd 9 07-18-2023 21:09:25
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
About the Author :
Sid-Ali Ouadfeul, Professor, Department of Geophysics, Geology and Reservoir Engineering, Algerian Petroleum Institute-IAP Corporate University, Algeria.
