Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. - Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful - Gain practical understanding of machine learning used in oil and gas operations through contributed case studies - Learn change management skills that will help gain confidence in pursuing the technology - Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)

Physics of Fluid Flow and Transport in Unconventional Reservoir Rocks Understanding and predicting fluid flow in hydrocarbon shale and other non-conventional reservoir rocks Oil and natural gas reservoirs found in shale and other tight and ultra-tight porous rocks have become increasingly important sources of energy in both North America and East Asia. As a result, extensive research in recent decades has focused on the mechanisms of fluid transfer within these reservoirs, which have complex pore networks at multiple scales. Continued research into these important energy sources requires detailed knowledge of the emerging theoretical and computational developments in this field. Following a multidisciplinary approach that combines engineering, geosciences and rock physics, Physics of Fluid Flow and Transport in Unconventional Reservoir Rocks provides both academic and industrial readers with a thorough grounding in this cutting-edge area of rock geology, combining an explanation of the underlying theories and models with practical applications in the field. Readers will also find: An introduction to the digital modeling of rocks Detailed treatment of digital rock physics, including decline curve analysis and non-Darcy flow Solutions for difficult-to-acquire measurements of key petrophysical characteristics such as shale wettability, effective permeability, stress sensitivity, and sweet spots Physics of Fluid Flow and Transport in Unconventional Reservoir Rocks is a fundamental resource for academic and industrial researchers in hydrocarbon exploration, fluid flow, and rock physics, as well as professionals in related fields.

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

This book constitutes the revised selected papers of the 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, held in Oak Ridge, TN, USA*, in August 2020. The 36 full papers and 1 short paper presented were carefully reviewed and selected from a total of 94 submissions. The papers are organized in topical sections of computational applications: converged HPC and artificial intelligence; system software: data infrastructure and life cycle; experimental/observational applications: use cases that drive requirements for AI and HPC convergence; deploying computation: on the road to a converged ecosystem; scientific data challenges. *The conference was held virtually due to the COVID-19 pandemic.

The Computed Tomography (CT) scanning of carbonate reservoirs is a non-destructive method used to obtain valuable information from reservoir rocks. This concise book covers all aspects of CT image analysis and their interpretation. It is focused on the CT scanned images and the data gathered from various carbonate reservoirs with different ages, from Paleozoic to Tertiary, and with different textures, degrees of heterogeneity, facies types, diagenetic impacts, fossil contents, sedimentary structures, reservoir quality, and fracturing. Numerous high-resolution images illustrate various aspects of carbonate rock properties and are suitable for analysis by both professionals and students. FEATURES Provides the first specialized book about core CT scanned image descriptions and explanations Contains supplementary data from various sources, such as image logs, cores, and thin section photos and petrophysical measurements Includes original CT images from different reservoirs Covers both research and industrial aspects of core CT scanning and is useful for comparing CT scanned images Uses original images and reservoir data gathered by the author This book can be used by professionals working in the oil and gas industry, researchers and academics studying petroleum-related disciplines, and students taking courses in petroleum geology and reservoir engineering. It can also be used in a range of related sciences, such as sedimentology, soil sciences, paleontology, petrology, engineering geology, study of porous materials, and hydrogeology.