Sustainable geoscience for natural gas sub-surface systems /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Wood, David A. (Editor ), Cai, Jianchao (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, MA : Gulf Professional Publishing, [2022]
Colección:Fundamentals and sustainable advances in natural gas science and engineering ; v. 2.
Temas:
Natural gas > Geology.
Engineering geology.
Mining geology.
Gaz naturel > G�eologie.
G�eologie appliqu�ee.
G�eologie mini�ere.
Engineering geology
Mining geology
Natural gas > Geology
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Sustainable Geoscience for Natural Gas SubSurface Systems
  • Copyright
  • Contents
  • Contributors
  • Preface
  • About The Fundamentals and Sustainable Advances in Natural Gas Science and Engineering Series
  • About this volume 2: Sustainable geoscience for natural gas subsurface systems
  • Chapter One: Pore-scale characterization and fractal analysis for gas migration mechanisms in shale gas reservoirs
  • 1. Introduction
  • 2. Pore-scale characterization from nitrogen adsorption-desorption data
  • 3. Pore-scale characterization from SEM data
  • 4. Definitions of fractal parameters
  • 5. Fractal analysis of nitrogen adsorption isotherms
  • 6. Fractal analysis of SEM images
  • 7. Pore-scale and core-scale gas transport mechanisms
  • 7.1. Gas transport in a single capillary
  • 7.2. Gas transport in fractal porous media
  • 8. Conclusions
  • Acknowledgments
  • References
  • Chapter Two: Three-dimensional gas property geological modeling and simulation
  • 1. Introduction
  • 2. 3D modeling
  • 3. Geological conditions of gas reservoirs
  • 4. Typical earth data used in modeling
  • 5. Modeling methods
  • 6. Structural modeling
  • 7. Facies modeling
  • 8. Petrophysical modeling
  • 9. Geomechanical modeling
  • 10. Volumetric modeling
  • 11. Case study
  • 12. 3D structural modeling
  • 13. 3D facies modeling
  • 14. 3D petrophysical modeling
  • 15. 3D geomechanical modeling
  • 16. Summary
  • References
  • Chapter Three: Acoustic, density, and seismic attribute analysis to aid gas detection and delineation of reservoir properties
  • 1. Introduction
  • 2. Natural gas reservoirs detection
  • 2.1. Poststack seismic attributes analysis
  • 2.1.1. Acoustic and velocity attributes: Direct gas indicators
  • 2.1.2. Bottom simulating reflector
  • 2.1.3. Gas chimneys
  • 2.1.4. Acoustic impedance
  • 2.1.5. Other seismic attributes.
  • 2.2. Prestack seismic attributes analysis
  • 3. Delineation and characterization of natural gas reservoirs
  • 3.1. Porosity
  • 3.2. Pore types
  • 3.3. Water saturation
  • 3.4. Hydraulic and electrical flow units
  • 3.5. Rock mechanical properties
  • 4. Summary
  • References
  • Chapter Four: Integrated microfacies interpretations of large natural gas reservoirs combining qualitative and quantitati ...
  • 1. Introduction
  • 2. Fundamental concepts and key principles
  • 2.1. Principals of petrographic analysis
  • 2.2. Thin section analysis
  • 2.3. SEM analysis
  • 2.4. The evolution of microfacies analysis
  • 3. Advanced research and detailed techniques
  • 3.1. Image preparation via histogram equalization
  • 3.2. Grain size determination and grain-size distributions
  • 3.3. Edges, features shapes, and boundaries detection
  • 3.4. Applying image arithmetic to enhance features of specific interest
  • 3.5. Gamma correction for birefringent minerals
  • 3.6. K-means clustering to isolate and quantify two-dimensional porosity and specific surface area
  • 3.7. Nearest neighbor (kNN) classifier facilitates features segmentation
  • 4. Gas field case studies
  • 4.1. South pars field
  • 4.2. Salman field
  • 4.3. Shah Deniz field
  • 5. Summary
  • Declarations
  • References
  • Chapter Five: Assessing the brittleness and total organic carbon of shale formations and their role in identifying optimu ...
  • 1. Introduction
  • 2. Fundamental concepts
  • 2.1. Estimating shale brittleness and ``fracability��
  • 2.2. Estimating total organic carbon from well-log data
  • 3. Advanced methods
  • 3.1. Machine learning approaches for predicting shale brittleness and TOC
  • 3.2. Advantages of transparency and correlation-free machine learning algorithms
  • 3.3. Optimizers suitable for TOB stage 2 predications.
  • 3.4. Measures of BI and TOC prediction accuracy assessed for shale assessment
  • 4. Case study: TOB machine learning to predict shale brittleness and TOC
  • 4.1. Characterization of two lower Barnett Shale Wells sections
  • 4.2. Results of TOB predictions of BIml and TOC for lower Barnett Shale Wells
  • 5. Summary
  • Declarations
  • References
  • Chapter Six: Shale kerogen kinetics from multiheating rate pyrolysis modeling with geological time-scale perspectives for ...
  • 1. Fundamental concepts
  • 1.1. Organic-rich shales and their gas and oil generation potential
  • 1.2. Types of kerogen and their associated gas and oil generation reactions
  • 1.3. Pyrolysis of organic-rich shales, kerogens and bitumens
  • 2. Advanced techniques and applications
  • 2.1. Modeling kerogen kinetics with the Arrhenius equation and its integral
  • 2.2. Procedure for matching pyrolysis S2 curves with calculated TTIARR and SigmaTTIARR values
  • 2.3. Controversy over methods used to fit multiheating rate shale pyrolysis S2 curves
  • 2.4. Combining reaction peaks generated by various E-A combinations
  • 2.5. Limitations of single-heating rate pyrolysis experiments
  • 3. Case study kinetic models for immature Duvernay shale Western Canada
  • 3.1. Case study overview
  • 3.2. Late Devonian Duvernay shale (Western Canada)
  • 3.3. Immature Duvernay shale sample SAP for reaction kinetic evaluations
  • 4. Summary
  • Declarations
  • References
  • Chapter Seven: Application of few-shot semisupervised deep learning in organic matter content logging evaluation
  • 1. Introduction
  • 2. Methodology
  • 2.1. ELM-SAE model structure
  • 2.2. Stacked ELM-SAE
  • 2.3. RBM
  • 2.4. DBM
  • 2.5. Bagging algorithm
  • 2.6. Network structure of the integrated deep learning model (IDLM)
  • 3. Samples and experiments
  • 3.1. Data sets and descriptions
  • 3.2. Training.
  • 3.2.1. Determination of hyperparameter (SELM-SAE)
  • 3.2.2. Determination of hyperparameter (DBM)
  • 3.2.3. Hyperparameter determination results for models including bagging
  • 4. Results: TOC Prediction comparisons for IDLM and other models
  • 5. Conclusions
  • Acknowledgment
  • References
  • Chapter Eight: Microseismic analysis to aid gas reservoir characterization
  • 1. Introduction
  • 2. Principle and workflow of microseismic monitoring
  • 2.1. Basic principles
  • 2.2. Technical workflow
  • 3. Advanced processing and interpretation techniques
  • 3.1. Processing
  • 3.1.1. Microseismic detection and location
  • 3.1.2. Source mechanism inversion
  • 3.1.3. Stress inversion
  • 3.2. Interpretation
  • 3.2.1. Reservoir interpretation
  • 3.2.2. Microseismic geomechanics
  • 4. Case studies
  • 4.1. Shale hydraulic fracturing
  • 4.2. Coal-bed methane reservoir
  • 5. Summary
  • Declarations
  • Acknowledgments
  • References
  • Chapter Nine: Coal-bed methane reservoir characterization using well-log data
  • 1. Introduction
  • 2. Fundamental concepts pertaining to CBM
  • 2.1. Estimating coal composition and rank using well-log data
  • 2.2. Estimating gas content, potential flow rates and recovery from coals with well-log data
  • 3. Advanced assessment of coal bed methane properties
  • 3.1. Coal structure and fracability
  • 3.2. A geomechanically derived brittleness index
  • 3.3. Horizontal stress regime influence on coal seam characteristics
  • 3.4. Assessing the structure of coal and its influences on fracability
  • 3.5. The presence of existing natural fractures improves coal fracability
  • 3.6. Machine learning to improve coal property predictions
  • 4. Case study: Assessing coal fracability based on well-log information
  • 4.1. Application of fracability indicators to actual coal seams.
  • 4.2. Application of geomechanical coefficients to classify coal structure
  • 5. Summary
  • Declarations
  • References
  • Chapter Ten: Characterization of gas hydrate reservoirs using well logs and X-ray CT scanning as resources and environmen ...
  • 1. Introduction
  • 2. Fundamental concepts and key principles
  • 2.1. Well logging
  • 2.2. X-ray CT scanning
  • 2.2.1. Gas hydrate pore habits in hydrate-bearing sediments
  • 2.2.2. Basic physical properties in hydrate-bearing sediments
  • 3. Advanced research/field applications
  • 3.1. Well logging and X-ray CT scanning combination
  • 3.2. X-ray CT based characterization of pore fractal characteristics in hydrate-bearing sediments
  • 3.2.1. Maximal pore diameter
  • 3.2.2. Pore area fractal dimension
  • 3.2.3. Tortuosity fractal dimension
  • 4. Case studies
  • 4.1. Archie's saturation exponent for well-log data interpretation
  • 4.2. Hydraulic permeability reduction in hydrate-bearing sediments
  • 5. Summary and conclusions
  • Acknowledgments
  • Declarations
  • References
  • Chapter Eleven: Assessing the sustainability of potential gas hydrate exploitation projects by integrating commercial, en ...
  • 1. Fundamental concepts
  • 1.1. The potential and challenges facing natural gas hydrates as resources for development
  • 1.1.1. Technical considerations
  • 1.1.2. Economic, environmental, infrastructure, and social considerations
  • 1.2. Multicriteria decision analysis (MCDA) techniques
  • 1.2.1. MCDA techniques typically applied
  • 1.2.2. ELECTRE
  • 1.2.3. TOPSIS (the order of preference by similarity to an ideal solution)
  • 2. Advanced TOPSIS techniques that incorporate uncertainty
  • 2.1. Crisp, fuzzy and intuitionistic mathematical alternatives
  • 2.2. Fuzzy TOPSIS calculations
  • 2.3. Fuzzy TOPSIS analysis incorporating objective entropy weighting.

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