| Description |
1 online resource |
|
text txt rdacontent |
|
computer c rdamedia |
|
online resource cr rdacarrier |
| Series |
The fundamentals and sustainable advances in natural gas science and engineering ; v. 2 |
|
Fundamentals and sustainable advances in natural gas science and engineering ; v. 2.
|
| Note |
Print version record. |
| Contents |
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. |
| Subject |
Natural gas -- Geology.
|
|
Engineering geology.
|
|
Mining geology.
|
|
Gaz naturel -- Géologie.
|
|
Géologie appliquée.
|
|
Géologie minière.
|
|
Engineering geology
|
|
Mining geology
|
|
Natural gas -- Geology
|
| Added Author |
Wood, David A., editor.
|
|
Cai, Jianchao, editor.
|
| Other Form: |
Print version: Sustainable geoscience for natural gas sub-surface systems. Cambridge, MA : Gulf Professional Publishing, [2022] 0323854656 9780323854658 (OCoLC)1250308720 |
|
Print version: SUSTAINABLE GEOSCIENCE FOR NATURAL GAS SUB-SURFACE SYSTEMS. [S.l.] : GULF PUBLISHING COMPANY, 2021 0323854656 (OCoLC)1250308720 |
| ISBN |
9780323854665 (electronic bk.) |
|
0323854664 (electronic bk.) |
|
9780323854658 |
| Standard No. |
AU@ 000070238782 |
|
