Unconventional Hydrocarbon Resources

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

• Preface xiii1 Predrill Pore Pressure Estimation in Shale Gas Reservoirs Using Seismic Genetic Inversion with an Example from the Barnett Shale 1Sid-Ali Ouadfeul, Mohamed Zinelabidine Doghmane, and Leila Aliouane1.1 Introduction 11.2 Methods and Application to Barnett Shale 21.2.1 Geological Setting 21.2.2 Methods 31.3 Data Processing 61.4 Results Interpretation and Conclusions 7References 92 An Analysis of the Barnett Shale’s Seismic Anisotropy’s Role in the Exploration of Shale Gas Reservoirs (United States) 11Sid-Ali Ouadfeul, Leila Aliouane, Mohamed Zinelabidine Doghmane, and Amar Boudella2.1 Introduction 112.2 Seismic Anisotropy 122.3 Application to Barnett Shale 142.3.1 Geological Setting 142.3.2 Data Analysis 152.4 Conclusions 18References 183 Wellbore Stability in Shale Gas Reservoirs with a Case Study from the Barnett Shale 21Sid-Ali Ouadfeul, Mohamed Zinelabidine Doghmane, and Leila Aliouane3.1 Introduction 213.2 Wellbore Stability 223.2.1 Mechanical Stress 223.2.2 Chemical Interactions with the Drilling Fluid 223.2.3 Physical Interactions with the Drilling Fluid 223.3 Pore Pressure Estimation Using the Eaton’s Model 233.4 Shale Play Geomechanics and Wellbore Stability 243.5 Application to Barnett Shale 263.5.1 Geological Context 263.5.2 Data Processing 283.6 Conclusion 28References 304 A Comparison of the Levenberg-Marquardt and Conjugate Gradient Learning Methods for Total Organic Carbon Prediction in the Barnett Shale Gas Reservoir 31Sid-Ali Ouadfeul, Mohamed Zinelabidine Doghmane, and Leila Aliouane4.1 Introduction 314.2 Levenberg-Marquardt Learning Algorithm 324.3 Application to Barnett Shale 334.3.1 Geological Setting 334.3.2 Data Processing 334.3.3 Results Interpretation 364.4 Conclusions 39References 405 Identifying Sweet Spots in Shale Reservoirs 41Susan Smith Nash5.1 Introduction 415.2 Materials and Methods 415.3 Data for Two Distinct Types of Sweet Spot Identification Workflows 425.3.1 Workflow 5.1: Early-Phase Workflow Elements: Total Petroleum System Approach 425.3.2 Workflow 5.2: Smaller-Scale Field-Level Tools and Techniques 435.4 Results: Two Integrative Workflows 455.4.1 Early-Phase Exploration Workflow 455.4.2 Later Phase Developmental, Including Refracing Workflow 455.5 Case Studies 465.5.1 Woodford Shale: Emphasis on Chemostratigraphy 465.5.2 Barnett Shale: Emphasis on Seismic Attributes 465.5.3 Eagle Ford Shale: Pattern Recognition/Deep Learning 475.6 Conclusion 47References 476 Surfactants in Shale Reservoirs 49Susan Smith Nash6.1 Introduction 496.2 Function of Surfactants 496.2.1 Drilling 506.2.2 Completion (Hydraulic Fracturing) 506.3 Materials and Methods 506.4 Characteristics of Shale Reservoirs 506.4.1 High Clay Mineral Content 516.4.2 Nano-Sized Pores 516.4.3 Mixed-Wettability Behavior 516.4.4 High Capillary Pressures 516.5 The Klinkenberg Correction 516.5.1 Klinkenberg Gas Slippage Measurement 526.6 Completion Chemicals to Consider in Addition to the Surfactant 526.6.1 Enhanced Oil Recovery (EOR) 526.6.2 Liquids-Rich Shale Plays After Initial Decline 536.7 Mono-Coating Proppant 536.7.1 Zwitterionic Coating 536.8 Dual-Coating Proppant 546.8.1 Outside Coating 546.8.2 Inner Coating 546.9 Dual Coating with Porous Proppant 546.9.1 Zwitterionic Outer Coating; Inorganic Salt Inner Coating, Porous Core 546.10 Data 556.10.1 Types of Surfactants 556.10.1.1 Anionic 556.10.1.2 Cationic 566.10.1.3 Nonionic 566.10.1.4 Zwitterionic 566.11 Examples of Surfactants in Shale Plays 566.11.1 Bakken (Wang and Xu 2012) 566.11.2 Eagle Ford (He and Xu 2017) 576.11.3 Utica (Shuler et al. 2016) 576.12 Results 576.13 Shale Reservoirs, Gas, and Adsorption 576.14 Operational Conditions 586.15 Conclusions 59References 597 Neuro-Fuzzy Algorithm Classification of Ordovician Tight Reservoir Facies in Algeria 61Mohamed Zinelabidine Doghmane, Sid-Ali Ouadfeul, and Leila Aliouane7.1 Introduction 617.2 Neuro-Fuzzy Classification 617.3 Results Discussion 637.4 Conclusion 67References 678 Recognition of Lithology Automatically Utilizing a New Artificial Neural Network Algorithm 69Mohamed Zinelabidine Doghmane, Sid-Ali Ouadfeul, and Leila Aliouane8.1 Introduction 698.2 Well-Logging Methods 708.2.1 Nuclear Well Logging 708.2.2 Neutron Well Logging 708.2.3 Sonic Well Logging 708.3 Use of ANN in the Oil Industry 718.4 Lithofacies Recognition 718.5 Log Interpretation 728.5.1 Methodology of Manual Interpretation 728.5.2 Results of Manual/Automatic Interpretation 738.6 Conclusion 78References 799 Construction of a New Model (ANNSVM) Compensator for the Low Resistivity Phenomena Saturation Computation Based on Logging Curves 81Mohamed Zinelabidine Doghmane, Sid-Ali Ouadfeul, and Leila Aliouane9.1 Introduction 819.2 Field Geological Description 829.2.1 Conventional Interpretation 829.2.2 Reservoir Mineralogy 849.3 Low-Resistivity Phenomenon 849.3.1 Cross Plots Interpretation 849.3.2 NMR Logs Interpretation 859.3.3 Comparison Between Well-1 and Well- 2 859.3.4 Developed Logging Tools 859.3.5 Proposed ANNSVM Algorithm 859.4 Conclusions 91References 9110 A Practical Workflow for Improving the Correlation of Sub-Seismic Geological Structures and Natural Fractures using Seismic Attributes 93Mohamed Zinelabidine Doghmane, Sid-Ali Ouadfeul, and Leila Aliouane10.1 Introduction 9310.2 Description of the Developed Workflow 9410.3 Discussion 9410.4 Conclusions 95References 9611 Calculation of Petrophysical Parameter Curves for Nonconventional Reservoir Modeling and Characterization 99Mohamed Zinelabidine Doghmane, Sid-Ali Ouadfeul, and Leila Aliouane11.1 Introduction 9911.2 Proposed Methods 9911.3 Results and Discussion 10111.4 Conclusions 101References 10212 Fuzzy Logic for Predicting Pore Pressure in Shale Gas Reservoirs With a Barnett Shale Application 105Leila Aliouane, Sid-Ali Ouadfeul, Mohamed Zinelabidine Doghmane, and Amar Boudella12.1 Introduction 10512.2 The Fuzzy Logic 10612.3 Application to Barnett Shale 10612.3.1 Geological Context 10612.3.2 Data Processing 10712.4 Results Interpretation and Conclusions 110References 11113 Using Well-Log Data, a Hidden Weight Optimization Method Neural Network Can Classify the Lithofacies of a Shale Gas Reservoir: Barnett Shale Application 113Leila Aliouane, Sid-Ali Ouadfeul, Mohamed Z. Doghmane, and Ammar Boudella13.1 Introduction 11313.2 Artificial Neural Network 11413.3 Hidden Weight Optimization Algorithm Neural 11413.4 Geological Context of the Barnett Shale 11513.5 Results Interpretation and Conclusions 117Bibliography 12414 The Use of Pore Effective Compressibility for Quantitative Evaluation of Low Resistive Pays 127Mohamed Zinelabidine Doghmane, Sid-Ali Ouadfeul, and Leila Aliouane14.1 Introduction 12714.2 Low-Resistivity Pays in the Studied Basin 12714.3 Water Saturation from Effective Pore Compressibility 12814.4 Discussion 12814.5 Conclusions 130Bibliography 13015 The Influence of Pore Levels on Reservoir Quality Based on Rock Typing: A Case Study of Quartzite El Hamra, Algeria 133Nettari Ferhat, Mohamed Z. Doghmane, Sid-Ali Ouadfeul, and Leila Aliouane15.1 Introduction 13315.2 Quick Scan Method 13315.3 Results 13515.4 Discussion 13515.5 Conclusions 137Bibliography 13716 An Example from the Algerian Sahara Illustrates the Use of the Hydraulic Flow Unit Technique to Discriminate Fluid Flow Routes in Confined Sand Reservoirs 139Abdellah Sokhal, Sid-Ali Ouadfeul, Mohamed Zinelabidine Doghmane, and Leila Aliouane16.1 Introduction 13916.2 Regional Geologic Setting 14016.3 Statement of the Problem 14216.3.1 Concept of HFU 14216.3.2 HFU Zonation Process 14216.4 Results and Discussion 14316.4.1 FZI Method 14316.4.2 FZI Method 14416.5 Conclusions 146References 1460005546230.indd 9 07-18-2023 21:09:2517 Integration of Rock Types and Hydraulic Flow Units for Reservoir Characterization. Application to Three Forks Formation, Williston Basin, North Dakota, USA 147Aldjia Boualam and Sofiane Djezzar17.1 Introduction 14717.2 Petrophysical Rock-Type Prediction 14817.3Rock Types’ Classification Based on R 35 Pore Throat Radius 15017.3.1 Upper Three Forks 15317.3.2 Middle Three Forks 15517.3.3 Lower Three Forks 15717.4 Determination of Hydraulic Flow Units 15717.4.1 Upper Three Forks 15917.4.2 Middle Three Forks 16017.4.3 Lower Three Forks 16017.5 Conclusion 160References 16218 Stress-Dependent Permeability and Porosity and Hysteresis. Application to the Three Forks Formation, Williston Basin, North Dakota, USA 163Aldjia Boualam and Sofiane Djezzar18.1 Introduction 16318.2 Database 16518.3 Testing Procedure 16618.3.1 Core Samples Cleaning and Drying 16718.3.2 Permeability and Porosity Measurements 16918.3.3 Mineral Composition Analysis 17018.3.4 Scanning Electron Microscope 17118.4 Results and Discussions 17418.4.1 Stress-Dependent Permeability and Hysteresis 17518.4.1.1 Upper Three Forks 17518.4.1.2 Middle Three Forks 18118.4.2 Permeability Evolution with Net Stress 18318.4.3 Stress-Dependent Porosity and Hysteresis 18618.4.3.1 Upper Three Forks 18618.4.3.2 Middle Three Forks 19218.4.4 Porosity Evolution with Net Stress 19418.4.5 Permeability Evolution with Porosity 19518.5 Conclusion 196References 19819 Petrophysical Analysis of Three Forks Formation in Williston Basin, North Dakota, USA 207Aldjia Boualam and Sofiane Djezzar19.1 Introduction 20719.2 Petrophysical Database 20819.2.1 Curve Editing and Environmental Correction 20919.2.2 Preanalysis Processing 21119.3 Methods and Background 21119.3.1 Wireline Logs 21119.3.1.1 Caliper Tool 21119.3.1.2 Total and Spectral Gamma-Ray Logs 21219.3.1.3 Electrical Properties (Resistivity) 21219.3.1.4 Neutron Logs 21319.3.1.5 Bulk Density Log 21319.3.1.6 Acoustic Logs 21319.3.1.7 Elemental Capture Spectroscopy 21419.3.1.8 Nuclear Magnetic Resonance 21519.3.1.9 Multifrequency Array Dielectric Measurements 21519.3.2 Petrophysical Analysis Challenges 21619.3.2.1 Formation Components and Volumes 21719.3.2.2 Water Saturation Model 22119.3.2.3 Nuclear Magnetic Resonance 22419.4 Petrophysical Analysis Results and Discussion 22419.4.1 Upper Three Forks 23119.4.2 Middle Three Forks 23619.4.3 Lower Three Forks 23719.5 Conclusion 240References 24120 Water Saturation Prediction Using Machine Learning and Deep Learning. Application to Three Forks Formation in Williston Basin, North Dakota, USA 251Aldjia Boualam and Sofiane Djezzar20.1 Introduction 25120.2 Experimental Procedure and Methodology 25320.2.1 Support Vector Machine Concepts 25320.2.2 Preprocessing of the Dataset 25520.2.3 Building SVR Model 25820.2.4 Building Random Forest Regression Model 26120.2.5 Building Deep Learning Model 26220.2.6 Curve Reconstruction Using K.Mod 26420.3 Results and Discussion 26420.4 Conclusion 275References 276Appendix Hysteresis Testing and Mineralogy 285Index 297