Geophysical Exploration for Hydrocarbon Reservoirs, Geothermal Energy, and Carbon Storage

Said Gaci, PhD, is the Director of Scientific and Technical Support to Research at Sonatrach’s Central Research and Development Directorate (DC R&D). His research interests include the application of signal processing and pattern recognition techniques in the geosciences.

• List of Contributors xviiPreface xixAbout the Book xxiSection I New Technologies and Insights into Petroleum Exploration 11 Gas Seepage in Marginal Structures as Additional Shallow and Deep Hydrocarbon Systems Indicator (Some of Recent FR Scanning Results) 3Valery Soloviev, Mykola Yakymchuk, Ignat Korchagin1.1 Introduction 31.2 General Principles and Methods 41.3 Gas Fluids as Additional Hydrocarbon Processes Indicator in Some Continental Margin Structures 41.3.1 Seepage Areas in the Central West Greenland Margin 51.3.2 Spitsbergen, Isfjorden Area 71.3.3 Area of Prince Karls Forland Island 91.3.4 Barents Sea areas 101.3.5 Northern Norwegian Barents Sea, Sentralbanken High Area 101.3.6 Borealis and Håkon Mosby Mud Volcanoes 101.3.6.1 Borealis Mud Volcano 101.3.6.2 The Håkon Mosby Mud Volcano 121.3.7 The North Sea Area 131.3.7.1 Well 6508/1-3 (SP3, Figure 1.8) 131.3.7.2 Well 16/5-7 (SP4, Figure 1.8) 131.3.7.3 Well 32/4-3 S (SP5, Figure 1.8) 141.3.7.4 Local Glengorm Well Drilling Site (SP21, Figure 1.8) 141.3.7.5 Nyegga Pockmarks (SP15, Figure 1.8) 141.3.7.6 The Area of the Emergency Well 2/4-14 (SP1, Figure 1.8) 151.3.7.7 Seepage Fields at the “Berta” Area (SP19, Figure 1.8) 161.3.7.8 Scanner Structure (SP16, Figure 1.8) 181.3.7.9 Pockmark Field in the Gulf of Patras, Ionian Sea 191.3.7.10 Some SP Scanning Results in the Black Sea 211.4 Conclusions 24Author Contributions 24Conflict of Interests 25References 252 The Role of the LVZ and of Increased Seismicity in the Localization of Abiogenic HC in the Crystalline Crust of Transcarpathia 29Valeriy Korchin, Elena Karnaukhova2.1 Introduction 292.2 Basic Principles of Petrophysical Thermobaric Modeling 302.3 Influence of РТ-Regimes on the Elastic Characteristics and Density of Rocks 302.4 The LVZs in the Crystalline Crust as Zones of Increased Porosity of Mineral Matter 342.5 A Comparison of Experimental Data and Geophysical Observations 362.6 Geological Interpretation of the PTBM Results 372.7 The Nature of LVZ along the DSS Profile (RP-17) Using the PTBM Methodology 392.8 Elastic Characteristics of the Mineral Substance along the DSS Profile (RP-17) 422.9 Conclusions 44References 453 Precambrian Mid-Continent Rift Potential for Hosting Numerous Helium and Hydrogen Accumulations, Central USA 49Steven A. Tedesco3.1 Introduction 503.2 The Formation of the Mid-Continent Rift System 513.3 Geology 523.4 Wells of Interest 573.5 Trap and Seal 593.5.1 Source Rocks 603.6 Gravity/Magnetics 613.7 Seismic 623.8 Oil and Gas Exploration and Production 643.9 Iron and Base Metals 653.10 Impact Craters 653.11 Helium 653.12 Hydrogen 673.13 Summary 70References 704 Production from Desmoinesian and Atokan Age Coalbed Methane and Carbonaceous Mudstone and Their Relationship to Structure and Geologic History of the Cherokee Basin, Kansas and Oklahoma, USA 73Steven A. Tedesco4.1 Introduction 734.2 Geology 754.2.1 Desorption, Adsorption, and Proximate Analysis 784.3 Production 794.4 Drilling and Completion Methods 844.5 Jefferson-Sycamore Area 854.6 Discussion 91References 975 Geophysical Research and Monitoring Within the Framework of a Block-Layered Model with Inclusions of a Hierarchical Structure 99Olga Hachay, Andrey Khachay5.1 Review 995.2 Conclusions 102References 102Section II Reservoir Characterization Concepts and Workflows 1056 A Review on Shear Wave Velocity Estimation Methods 107Said Gaci, Mohammed Farfour6.1 Introduction 1076.2 Empirical Relationships for Estimating S-Wave Velocity 1086.3 Intelligent Systems for Estimating S-Wave Velocity 1116.4 Rock Physics Models for Estimating S-Wave Velocity 1136.5 Example 1166.6 Conclusions 119References 1197 Geomechanics in Petroleum Exploration, Development, and Energy Transition 125Ghoulem Ifrene, Kuldeep Singh7.1 Introduction 1257.2 Role of Geomechanics in Exploration and Development 1267.2.1 Formation Pressure Prediction 1277.2.2 Wellbore Stability Analysis 1287.2.3 Tensile Fracture Initiation Mechanisms 1297.3 Enhancing Reservoir Performance Through Geomechanics 1307.3.1 Performance Evaluation of Fractured Reservoirs 1307.4 Predictive Analyses and Production Optimization 1317.4.1 Sand Production Mechanism Analysis 1317.4.2 Early Warning of Casing Failure 1337.5 Unconventional Hydrocarbon Reservoirs and Geomechanics 1347.5.1 Shale Gas and Shale Oil 1347.5.2 Heavy Oil Sands 1367.5.3 Gas Hydrates 1377.6 Geomechanics in Geological Carbon Storage 1407.6.1 Importance and Challenges 1407.6.2 Potential for Fault Reactivation and Induced Seismicity 1427.6.3 Numerical Modeling for Site Suitability and Injection Parameters 1457.7 Geomechanics of Hydrogen Storage and Production 1457.7.1 Geomechanical Properties of Hydrogen Storage Reservoirs 1467.7.2 Stress and Strain Considerations During Hydrogen Storage 1477.7.3 Geomechanical Modeling of Hydrogen Injection and Storage 1487.7.4 Current Challenges and Future Directions 1487.8 Conclusions 149References 1508 Size Scaling and Spatial Clustering of Natural Fracture Networks Using Fractal Analysis 161Sofiane Djezzar, Aldjia Boualam8.1 Introduction 1618.2 Geological Settings 1628.3 Methods and Approaches 1638.4 Fractal Analysis 1648.4.1 First Approach 1648.4.2 Second Approach 1658.4.2.1 The Basement Formation 1678.4.2.2 The Ajjers Formation 1708.4.2.3 In-Tahouite Formation 1708.4.2.4 Tamadjert Formation 1748.4.3 The Third Approach 1758.4.4 The Fourth Approach 1758.5 Conclusions 178References 1829 Application of Seismic Attributes on Digital Elevation Model: Fractures Detection and Reservoir Implication 185Sofiane Djezzar, Aldjia Boualam9.1 Introduction 1859.2 Problematic 1869.3 Workflow and Methodology 1879.4 Fault Detection Techniques 1909.5 Fault Analysis 1919.5.1 Major Faults Analysis 1929.5.2 Minor Faults Analysis 1939.6 Fracture Intensity and Density Analysis 1969.7 Fracture Connectivity, Permeability, and Wavelet Analysis 1979.8 Discussion 1999.9 Conclusions 200References 20110 Structural Analysis and Fracture Kinematics Using Seismic 2D and Geological Maps 205Sofiane Djezzar, Aldjia Boualam10.1 Introduction 20510.2 Material and Methods 20610.3 Geological Settings 20710.4 Gravity Data 20810.5 Structural Analysis 21010.6 Seismic Data Analysis 21210.7 Fault Analysis 21610.8 Conclusions 217References 21811 A New Method for Reservoir Fracture Characterization and Modeling Using Surface Analog 221Sofiane Djezzar, Aldjia Boualam11.1 Introduction 22111.2 Methodology 22211.3 Geological Background 22311.4 Material and Methods 22411.5 Data Analysis 22511.6 3D Fracture Models 23211.7 Discussion and Conclusions 233References 23512 An Integrated Workflow for Multiscale Fracture Analysis in Reservoir Analog 237Sofiane Djezzar, Aldjia Boualam12.1 Introduction 23712.2 Geological Background 23812.3 Material and Method 24012.4 Fracture Characterization 24112.4.1 Detection of Major Faults 24312.4.2 Detection of Minor Faults 24412.4.2.1 Basement Formation 24412.4.2.2 Ajjers Formation 24512.4.2.3 In-Tahouite Formation 24512.4.2.4 Tamadjert Formation 24612.5 Fracture Analysis 24612.6 Fractal Analysis 24812.7 3D Fault Models 24912.8 Discussion 24912.9 Conclusions 251References 251Section III Artificial Intelligence Applied to Reservoir Characterization 25713 Exploring the Depths: Satellite Image Processing and Artificial Intelligence in the Oil and Gas Industry 259Hasna Yazid, Said Gaci13.1 Introduction 25913.2 Overview of Satellite Technology 26013.2.1 Definition and Basic Principles 26013.2.2 Types of Satellites 26013.2.2.1 Geostationary Satellites 26013.2.2.2 Polar Orbiting Satellites 26013.2.2.3 Low Earth Orbit Satellites 26013.2.3 Sensors and Data Products 26113.2.3.1 Optical Sensors 26113.2.3.2 Synthetic Aperture Radar Sensors 26113.2.3.3 Thermal Infrared Sensors 26113.2.3.4 Hyperspectral Sensors 26113.3 Evolution of Satellite Technology in the Oil and Gas Industry 26113.4 Satellite Image Processing Techniques 26213.4.1 Preprocessing and Image Enhancement 26213.4.1.1 Radiometric and Atmospheric Corrections 26313.4.1.2 Geometric Corrections 26313.4.2 Feature Extraction and Analysis 26313.4.2.1 Texture Analysis 26313.4.2.2 Object-Based Image Analysis 26313.4.3 Classification Algorithms 26313.4.3.1 Supervised Classification 26313.4.3.2 Unsupervised Classification 26413.4.4 Image Segmentation Techniques 26413.4.4.1 Region Growing 26413.4.4.2 Watershed Segmentation 26413.5 Artificial Intelligence in Satellite Imagery Processing 26413.5.1 Introduction to AI and ml 26513.5.2 Deep Learning and Convolutional Neural Networks 26513.5.3 Object Detection and Feature Identification 26513.5.4 Challenges and Mitigation Strategies in AI-Based Processing 26513.6 Practical Applications and AI in the Oil and Gas Industry 26613.6.1 Exploration and Mapping 26613.6.1.1 Geological and Topographic Mapping 26613.6.1.2 Mapping and Identification of Rock Outcrops 26713.6.1.3 Forecasting Hydrocarbon Deposit Locations 26713.6.1.4 Managing Offshore Exploration and Development Operations 26813.6.2 Drilling and Well Planning 26813.6.3 Pipeline Monitoring and Leak Detection 26813.6.4 Environmental Monitoring and Compliance 26913.6.5 Operational Optimization and Cost Reduction 27213.7 Conclusions 27313.7.1 Summary of Key Points 27313.7.2 Future Directions and Emerging Technologies 274References 27514 Modern AI Usage in the Oil and Gas Industry for Reservoir Characterization and Lithofacies Forecasting (Rock Typing) 281Hasna Yazid, Said Gaci, Mohammed Farfour14.1 Introduction 28114.2 Workflow of Rock Typing Using Machine Learning 28414.3 Application 28514.3.1 Used Dataset 28514.3.2 Data Preparation 28514.3.2.1 Data Cleaning 28514.3.2.2 Data Normalization 28614.3.2.3 Principal Component Analysis 28614.3.2.4 Class Distribution in the Dataset 28614.3.2.5 Choice of Parameters 28614.3.2.6 Recursive Feature Elimination Selection Algorithm 28614.3.2.7 Selection of Machine Learning Algorithms 28614.3.3 Data Augmentation 29114.3.3.1 Preparing the Dataset for Data Augmentation 29214.3.3.2 Type of Data Used in the Use Case 29214.3.4 Data Analysis 29214.3.5 Tests and Results 29614.4 Conclusions 297Acknowledgment 299References 29915 Logging-Data-Driven Fluid Prediction in Clastic Reservoir Based on Fractal Attributes and Machine Learning Methods 303Abdelbasset Boulassel, Soraya Makhlouf, Fethi Ali Cheddad, Zinelaabidine Boumelit, Badis Zegagh, Salah Boufenchouche, Amar Boudella, Naima Zaourar, Said Gaci15.1 Introduction 30315.2 Studied Dataset 30415.3 Overview of Fractal Analysis Steps Employed in Geophysical Well Logs Study 30515.3.1 Key Steps in Fractal Analysis of Geophysical Well Logs 30515.4 Overview of Employed Machine Learning Methods 30715.4.1 Logistic Regression 30715.4.2 Random Forest 30715.4.3 Support Vector Machine 30815.4.4 K-Nearest Neighbors 30815.4.5 Discriminant Analysis 30915.5 Model Evaluation 30915.5.1 Confusion Matrix 30915.5.2 Accuracy 31015.5.3 Precision 31015.5.4 Recall (Sensitivity) 31015.5.5 Correct Classification 31015.5.6 Misclassification 31115.5.7 F-Score (F1-Score) 31115.5.8 ROC (Receiver Operating Characteristic) Curve 31115.5.9 AUC (Area Under the ROC Curve) 31115.6 Results and Discussion 31115.6.1 Training and Validation Results 31115.6.2 Results Interpretation 31215.6.3 Detailed Interpretation of Random Forest’s Results 31315.7 Conclusions 318Acknowledgment 318References 31816 Unlocking Deeper Insights: Using Machine Learning to Predict Dynamic Shear Wave Slowness from Well Logs 323Abdelbasset Boulassel, Soraya Makhlouf, Zinelaabidine Boumelit, Badis Zegagh, Salah Boufenchouche, Fethi Ali Cheddad, Amar Boudella, Naima Zaourar, Said Gaci16.1 Introduction 32316.2 Studied Wells and Dataset 32416.3 Overview of Employed Machine Learning Methods 32516.3.1 Extreme Gradient Boosting 32516.3.2 Random Forest 32616.3.3 Support Vector Machine 32616.3.4 K-Nearest Neighbors 32716.4 Model Evaluation 32816.4.1 Mean Absolute Error 32816.4.2 Mean Squared Error 32816.4.3 Coefficient of Determination (R 2) 32816.4.4 Adjusted R 2 32916.4.5 Akaike Information Criterion 32916.4.6 Schwarz Bayesian Criterion or Bayesian Information Criterion 32916.4.7 Interpretation of Metrics 32916.5 Results and Discussion 33016.5.1 Training and Validation Results 33016.5.2 Analysis and Interpretation of Prediction Results for Well WP- 1 33116.5.3 Analysis and Interpretation of Prediction Results for Well WP- 2 33416.6 Conclusions 338Acknowledgment 338References 338Section IV Energy transition: New Perspectives on Geothermal Energy Exploration and Development and CO2 Sequestration 34317 Energy Transition and the Role of AI: Statistics, Trends, and Implications 345Said Gaci, Hasna Yazid, Aziz Khelalef , Mohammed Farfour17.1 Introduction 34517.2 Objectives for the Energy Transition 34517.3 Emerging Trends of Energy Transition and AI 34617.3.1 Optimization of Energy Generation and Consumption 34617.3.2 Integration of Renewable Energy into Grids 34717.3.3 Speeding Up the Decarbonization Process 34717.3.4 Advanced Business Strategies and Policy Development 34717.4 Implications of Leveraging AI in Energy Transition 34817.5 Challenges to Apply AI in Renewable Energy Sector 34817.5.1 Quality and Accessibility of Data 34817.5.2 Computing Capacity and Environmental Impact 34817.5.3 Integration and Interoperability with Existing Systems 34917.5.4 Skilled Professionals’ Shortage and Employees Training 34917.5.5 Economic Considerations 34917.5.6 Data Security and Privacy 35017.5.7 Model Complexity 35017.5.8 Difficulties with Regulation and Policy 35017.5.9 Ethical Issues 35117.5.10 Opposition to Change 35117.5.11 Scalability 35117.6 Conclusions 351References 35218 On the Importance of Integrating Geomodeling in Geothermal Studies 355Mohamed Amrouche18.1 Introduction 35518.2 Geology of Geothermal Provinces 35618.3 Exploration of Geothermal Reservoirs 35818.3.1 Geological Methods 35818.3.2 Geochemical Methods 35818.3.3 Geophysical Methods 35918.3.3.1 Seismic Methods 35918.3.3.2 Electrical Methods 35918.3.3.3 Gravity and Magnetic Methods 35918.3.3.4 Distributed Acoustic Sensing (DAS) Methods 35918.3.4 Well Logging Methods 36018.3.5 Geomechanical Methods 36018.3.6 Geostatistical Methods 36018.4 Modeling the Subsurface of Geothermal Reservoirs 36118.5 Concepts of 3D Geocellular Modeling 36318.6 Geophysical Modeling with the 3D Geocellular Grid 36718.7 Faults and Fracture Network Modeling with the 3D Geocellular Grid 37018.8 Updating the Property Models with Integrated Workflows 37318.9 Conclusions 374References 37519 Advancements, Challenges, and Outlook of Geothermal Reservoir Operations 379Ghoulem Ifrene, Singh Kuldeep, William Gosnold19.1 Introduction 37919.2 Geomechanical Considerations of Geothermal Reservoirs 38219.2.1 Geothermal Reservoir Characterization 38219.2.1.1 Geological Characterization 38219.2.1.2 Geophysical Techniques 38319.2.1.3 Wellbore Data Acquisition 38319.2.1.4 Reservoir Engineering Analysis 38419.2.1.5 Limitations and Mitigating Strategies for Geothermal Reservoir Characterization 38419.2.2 Rock Mechanics Issues in Geothermal Development 38519.2.3 Fracture Characterization and Its Role in Heat Extraction 38619.2.4 Managing Induced Seismicity in Geothermal Operations 38719.3 Drilling and Well Completion Technologies 39019.3.1 Innovations in Drilling: Embracing Fishbone Techniques for Enhanced Geothermal Systems 39019.3.2 Comparing Fishbone and Conventional Drilling Methods 39019.4 Production and Injection Optimization 39219.4.1 Optimizing Geothermal Operations with Fishbone Drilling 39219.4.2 Advanced Modeling and Simulation Techniques 39419.4.2.1 Advanced Modeling Considerations 39419.4.2.2 Advanced Modeling Applications 39519.4.2.3 Future Trends in Modeling and Simulation 39519.4.3 Optimization of Production and Injection Strategies 39719.4.3.1 Optimization of Injection and Production Parameters 39719.4.3.2 Optimization of Stimulation Strategies 39719.4.3.3 Cyclic Injection 39819.5 Future Directions and Research Needs 39819.5.1 The Potential Usage of CO2 as Injection Fluid for EGS 39819.5.1.1 Challenges of Using CO₂ in Geothermal Systems 40019.5.2 Emerging Technologies 40119.6 Environmental and Social Considerations for Geothermal Energy Development 40319.6.1 Challenges and Opportunities in Sustainable Energy Practices 403References 40920 Multiscale Reservoir Characterization of a CO2 Storage Aquifer: Mineralogical, Geomechanical, and Petrophysical Analyses for a CCS Project in North Dakota? 417Aimen Laalam, Ahmed Merzoug, Hichem Aymen Katib Chellal20.1 Introduction 41720.2 CCS Overview 41820.2.1 CCS Project Phases 41820.2.1.1 Carbon Capture 41820.2.1.2 CO2 Transport 41920.2.1.3 CO2 Storage 41920.2.2 Major Successful CCUS Projects 42020.2.3 Carbon Storage in Saline Aquifers 42120.2.4 Machine Learning for CCS 42220.3 Case Study: Carbon Storage in the Broom Creek Saline Aquifer, Williston Basin, North Dakota 42520.3.1 Overview of the Study Area 42620.3.2 Data Sources 42720.3.3 Geophysical Well Logs Interpretation 42820.3.4 Core Analysis 43020.3.5 Mineralogy 43220.3.6 Geomechanical Evaluation 43620.3.6.1 Geomechanics-Flow Coupled Simulation Model Description 43920.3.6.2 Open Boundary Model 44020.3.6.3 Closed Boundary Model 44220.3.6.4 Discussion 44320.4 Conclusions 444References 44421 Anthropogenic Carbon Sequestration into the Subsurface: Caveats and Pitfalls 451Steven A. Tedesco21.1 Introduction 45121.2 CO2 Incentives 45221.3 Chemistry 45221.4 Carbon Dioxide 45321.5 Potential Sequestration Locations 45521.6 Sequestration in Hydrocarbon and Carbon Dioxide Reservoirs 45621.7 Risk Assessment Analysis and Characterization of a Reservoir for CO2 Sequestration 45821.8 Sequestration in Saline Aquifers 46121.9 Sequestration in Coal Seams 46521.10 Sequestration in Carbonaceous Mudstones 46821.11 Mineral Sequestration 47021.12 Sequestration in Oceans 47121.13 Sequestration in Soils 47121.14 Class VI Wells 47121.15 Case Histories/Models 47321.15.1 Farnsworth Unit 47321.15.2 Aquistore CO2 Sequestration Site 47321.15.3 Sleipner Field 47521.15.4 Stenlille Gas Storage 47921.16 Summary 482References 483Index 489