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.
1. Pore-scale characterization and fractal analysis for gas migration mechanisms in shale gas reservoirs 2. 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
David A. Wood has more than forty years of international gas, oil, and broader energy experience since gaining his Ph.D. in geosciences from Imperial College London in the 1970s. His expertise covers multiple fields including subsurface geoscience and engineering relating to oil and gas exploration and production, energy supply chain technologies, and efficiencies. For the past two decades, David has worked as an independent international consultant, researcher, training provider, and expert witness. He has published an extensive body of work on geoscience, engineering, energy, and machine learning topics. He currently consults and conducts research on a variety of technical and commercial aspects of energy and environmental issues through his consultancy, DWA Energy Limited. He has extensive editorial experience as a founding editor of Elsevier’s Journal of Natural Gas Science & Engineering in 2008/9 then serving as Editor-in-Chief from 2013 to 2016. He is currently Co-Editor-in-Chief of Advances in Geo-Energy Research. Jianchao Cai received his B.Sc in Physics from Henan Normal University and MSc and Ph.D in Condensed Matter Physics from Huazhong University of Science and Technology. He is currently a professor at the Institute of Geophysics and Geomatics at the China University of Geosciences (Wuhan). Meanwhile, he serves as Associate Editor or Editorial member for several journals including Journal of Natural Gas Science & Engineering, International Journal of Oil, Gas and Coal Technology, Fractals. He has published more than 130 journal articles, two books, and numerous book chapters.