Sustainable Geoscience for Natural Gas SubSurface Systems delivers many of the scientific fundamentals needed in the natural gas industry, including coal-seam gas reservoir characterization and fracture analysis modeling for shale and tight gas reservoirs. Advanced research includes machine learning applications for well log and facies analysis, 3D gas property geological modeling, and X-ray CT scanning to reduce environmental hazards. Supported by corporate and academic contributors, along with two well-distinguished editors, the book gives today's natural gas engineers both fundamentals and advances in a convenient resource, with a zero-carbon future in mind.
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Table of Contents
1. Pore-scale characterization and fractal analysis for gas migration mechanisms in shale gas reservoirs2. Three-dimensional gas property geological modelling and simulation
3. Acoustic, density and seismic attribute analysis to aid gas detection and delineation of reservoir properties
4. Integrated microfacies interpretations of large natural gas reservoirs combining qualitative and quantitative image analysis
5. Brittleness index predictions from Lower Barnett shale well-log data applying an optimized data matching algorithm at various sampling densities
6. Shale kerogen kinetics from multi-heating rate pyrolysis modelling with geological time-scale perspectives for petroleum generation
7. Application of few-shot semi-supervised deep learning in organic matter content logging evaluation
8. Microseismic analysis to aid gas reservoir characterization
9. Coal-bed methane reservoir characterization using well-log data
10. Characterization of gas hydrate reservoirs using well logs and X-ray CT scanning as resources and environmental hazards
11. Assessing the sustainability of potential gas hydrate exploitation projects by integrating commercial, environmental, social and technical considerations
12. Gas adsorption and reserve estimation for conventional and unconventional gas resources
13. Dataset Insight and Variable Influences Established Using Correlations, Regressions and Transparent Customized Formula Optimization
