Download or read book Reservoir Characterization, Modeling and Quantitative Interpretation written by Shib Sankar Ganguli and published by Elsevier. This book was released on 2023-10-27 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reservoir Characterization, Modeling and Quantitative Interpretation: Recent Workflows to Emerging Technologies offers a wide spectrum of reservoir characterization techniques and technologies, focusing on the latest breakthroughs and most efficient methodologies in hydrocarbon exploration and development. Topics covered include 4D seismic technologies, AVAz inversion, fracture characterization, multiscale imaging technologies, static and dynamic reservoir characterization, among others. The content is delivered through an inductive approach, which will help readers gain comprehensive insights on advanced practices and be able to relate them to other subareas of reservoir characterization, including CO2 storage and data-driven modeling. This will be especially useful for field scientists in collecting and analyzing field data, prospect evaluation, developing reservoir models, and adopting new technologies to mitigate exploration risk. They will be able to solve the practical and challenging problems faced in the field of reservoir characterization, as it will offer systematic industrial workflows covering every aspect of this branch of Earth Science, including subsurface geoscientific perspectives of carbon geosequestration. This resource is a 21st Century guide for exploration geologists, geoscience students at postgraduate level and above, and petrophysicists working in the oil and gas industry. Covers the latest and most effective technologies in reservoir characterization, including Avo analysis, AVAz inversion, wave field separation and Machine Learning techniques Provides a balanced blend of both theoretical and practical approaches for solving challenges in reservoir characterization Includes detailed industry-standard practical workflows, along with code structures for algorithms and practice exercises