Overview

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.

Full Product Details

Author:   Sid-Ali Ouadfeul (Algerian Petroleum Institute-IAP Corporate University, Algeria)
Publisher:   John Wiley & Sons Inc
Imprint:   John Wiley & Sons Inc
Weight:   0.916kg
ISBN:  

9781119389361

ISBN 10:   1119389364
Pages:   320
Publication Date:   24 August 2023
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
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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

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

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