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

Chapter 1: Gas Seepage in Marginal Structures as Additional Shallow and Deep Hydrocarbon Systems Indicator (Some of Recent FR Scanning Results)

• • 1.1 Introduction
• • 1.2 General Principles and Methods
• • 1.3 Gas Fluids as Additional Hydrocarbon Processes Indicator in Some Continental Margin Structures
• • 1.4 Conclusions

Chapter 2: The Role of the LVZ and of Increased Seismicity in the Localization of Abiogenic HC in the Crystalline Crust of Transcarpathia

• • 2.1 Introduction
• • 2.2 Basic Principles of Petrophysical Thermobaric Modeling
• • 2.3 Influence of ÐÒ-Regimes on the Elastic Characteristics and Density of Rocks
• • 2.4 The LVZs in the Crystalline Crust as Zones of Increased Porosity of Mineral Matter
• • 2.5 A Comparison of Experimental Data and Geophysical Observations
• • 2.6 Geological Interpretation of the PTBM Results
• • 2.7 The Nature of LVZ along the DSS Profile (RP-17) Using the PTBM Methodology
• • 2.8 Elastic Characteristics of the Mineral Substance along the DSS Profile (RP-17)
• • 2.9 Conclusions

Chapter 3: Precambrian Mid-Continent Rift Potential for Hosting Numerous Helium and Hydrogen Accumulations, Central USA

• • 3.1 Introduction
• • 3.2 The Formation of the Mid-Continent Rift System
• • 3.3 Geology
• • 3.4 Wells of Interest
• • 3.5 Trap and Seal
• • 3.6 Gravity/Magnetics
• • 3.7 Seismic
• • 3.8 Oil and Gas Exploration and Production
• • 3.9 Iron and Base Metals
• • 3.10 Impact Craters
• • 3.11 Helium
• • 3.12 Hydrogen
• • 3.13 Summary

Chapter 4: 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

• • 4.1 Introduction
• • 4.2 Geology
• • 4.3 Production
• • 4.4 Drilling and Completion Methods
• • 4.5 Jefferson-Sycamore Area
• • 4.6 Discussion

Chapter 5: Geophysical Research and Monitoring Within the Framework of a Block-Layered Model with Inclusions of a Hierarchical Structure

• • 5.1 Review
• • 5.2 Conclusions

Chapter 6: A Review on Shear Wave Velocity Estimation Methods

• • 6.1 Introduction
• • 6.2 Empirical Relationships for Estimating S-Wave Velocity
• • 6.3 Intelligent Systems for Estimating S-Wave Velocity
• • 6.4 Rock Physics Models for Estimating S-Wave Velocity
• • 6.5 Example
• • 6.6 Conclusions

Chapter 7: Geomechanics in Petroleum Exploration, Development, and Energy Transition

• • 7.1 Introduction
• • 7.2 Role of Geomechanics in Exploration and Development
• • 7.3 Enhancing Reservoir Performance Through Geomechanics
• • 7.4 Predictive Analyses and Production Optimization
• • 7.5 Unconventional Hydrocarbon Reservoirs and Geomechanics
• • 7.6 Geomechanics in Geological Carbon Storage
• • 7.7 Geomechanics of Hydrogen Storage and Production
• • 7.8 Conclusions

Chapter 8: Size Scaling and Spatial Clustering of Natural Fracture Networks Using Fractal Analysis

• • 8.1 Introduction
• • 8.2 Geological Settings
• • 8.3 Methods and Approaches
• • 8.4 Fractal Analysis
• • 8.5 Conclusions

Chapter 9: Application of Seismic Attributes on Digital Elevation Model: Fractures Detection and Reservoir Implication

• • 9.1 Introduction
• • 9.2 Problematic
• • 9.3 Workflow and Methodology
• • 9.4 Fault Detection Techniques
• • 9.5 Fault Analysis
• • 9.6 Fracture Intensity and Density Analysis
• • 9.7 Fracture Connectivity, Permeability, and Wavelet Analysis
• • 9.8 Discussion
• • 9.9 Conclusions

Chapter 10: Structural Analysis and Fracture Kinematics Using Seismic 2D and Geological Maps

• • 10.1 Introduction
• • 10.2 Material and Methods
• • 10.3 Geological Settings
• • 10.4 Gravity Data
• • 10.5 Structural Analysis
• • 10.6 Seismic Data Analysis
• • 10.7 Fault Analysis
• • 10.8 Conclusions

Chapter 11: A New Method for Reservoir Fracture Characterization and Modeling Using Surface Analog

• • 11.1 Introduction
• • 11.2 Methodology
• • 11.3 Geological Background
• • 11.4 Material and Methods
• • 11.5 Data Analysis
• • 11.6 3D Fracture Models
• • 11.7 Discussion and Conclusions

Chapter 12: An Integrated Workflow for Multiscale Fracture Analysis in Reservoir Analog

• • 12.1 Introduction
• • 12.2 Geological Background
• • 12.3 Material and Method
• • 12.4 Fracture Characterization
• • 12.5 Fracture Analysis
• • 12.6 Fractal Analysis
• • 12.7 3D Fault Models
• • 12.8 Discussion
• • 12.9 Conclusions

Chapter 13: Exploring the Depths: Satellite Image Processing and Artificial Intelligence in the Oil and Gas Industry

• • 13.1 Introduction
• • 13.2 Overview of Satellite Technology
• • 13.3 Evolution of Satellite Technology in the Oil and Gas Industry
• • 13.4 Satellite Image Processing Techniques
• • 13.5 Artificial Intelligence in Satellite Imagery Processing
• • 13.6 Practical Applications and AI in the Oil and Gas Industry
• • 13.7 Conclusions

Chapter 14: Modern AI Usage in the Oil and Gas Industry for Reservoir Characterization and Lithofacies Forecasting (Rock Typing)

• • 14.1 Introduction
• • 14.2 Workflow of Rock Typing Using Machine Learning
• • 14.3 Application
• • 14.4 Conclusions

Chapter 15: Logging-Data-Driven Fluid Prediction in Clastic Reservoir Based on Fractal Attributes and Machine Learning Methods

• • 15.1 Introduction
• • 15.2 Studied Dataset
• • 15.3 Overview of Fractal Analysis Steps Employed in Geophysical Well Logs Study
• • 15.4 Overview of Employed Machine Learning Methods
• • 15.5 Model Evaluation
• • 15.6 Results and Discussion
• • 15.7 Conclusions

Chapter 16: Unlocking Deeper Insights: Using Machine Learning to Predict Dynamic Shear Wave Slowness from Well Logs

• • 16.1 Introduction
• • 16.2 Studied Wells and Dataset
• • 16.3 Overview of Employed Machine Learning Methods
• • 16.4 Model Evaluation
• • 16.5 Results and Discussion
• • 16.6 Conclusions

Chapter 17: Energy Transition and the Role of AI: Statistics, Trends, and Implications

• • 17.1 Introduction
• • 17.2 Objectives for the Energy Transition
• • 17.3 Emerging Trends of Energy Transition and AI
• • 17.4 Implications of Leveraging AI in Energy Transition
• • 17.5 Challenges to Apply AI in Renewable Energy Sector
• • 17.6 Conclusions

Chapter 18: On the Importance of Integrating Geomodeling in Geothermal Studies

• • 18.1 Introduction
• • 18.2 Geology of Geothermal Provinces
• • 18.3 Exploration of Geothermal Reservoirs
• • 18.4 Modeling the Subsurface of Geothermal Reservoirs
• • 18.5 Concepts of 3D Geocellular Modeling
• • 18.6 Geophysical Modeling with the 3D Geocellular Grid
• • 18.7 Faults and Fracture Network Modeling with the 3D Geocellular Grid
• • 18.8 Updating the Property Models with Integrated Workflows
• • 18.9 Conclusions

Chapter 19: Advancements, Challenges, and Outlook of Geothermal Reservoir Operations

• • 19.1 Introduction
• • 19.2 Geomechanical Considerations of Geothermal Reservoirs
• • 19.3 Drilling and Well Completion Technologies
• • 19.4 Production and Injection Optimization
• • 19.5 Future Directions and Research Needs
• • 19.6 Environmental and Social Considerations for Geothermal Energy Development

Chapter 20: Multiscale Reservoir Characterization of a CO 2 Storage Aquifer: Mineralogical, Geomechanical, and Petrophysical Analyses for a CCS Project in North Dakota?

• • 20.1 Introduction
• • 20.2 CCS Overview
• • 20.3 Case Study: Carbon Storage in the Broom Creek Saline Aquifer, Williston Basin, North Dakota
• • 20.4 Conclusions

Chapter 21: Anthropogenic Carbon Sequestration into the Subsurface: Caveats and Pitfalls

• • 21.1 Introduction
• • 21.2 CO 2 Incentives
• • 21.3 Chemistry
• • 21.4 Carbon Dioxide
• • 21.5 Potential Sequestration Locations
• • 21.6 Sequestration in Hydrocarbon and Carbon Dioxide Reservoirs
• • 21.7 Risk Assessment Analysis and Characterization of a Reservoir for CO 2 Sequestration
• • 21.8 Sequestration in Saline Aquifers
• • 21.9 Sequestration in Coal Seams
• • 21.10 Sequestration in Carbonaceous Mudstones
• • 21.11 Mineral Sequestration
• • 21.12 Sequestration in Oceans