Advances in Subsurface Data Analytics, Bhattacharya Shuvajit, Di Haibin

This reference teaches how to do more with less. Used to develop tools and techniques of data-driven predictive modelling and machine learning for subsurface engineering and science, engineers will be introduced to methods of generating subsurface signals and analyzing the complex relationships within various subsurface signals using machine learning. Algorithmic procedures in MATLAB, R, PYTHON, and TENSORFLOW are displayed in text and through online instructional video to assist training and learning. Field cases are also presented to understand real-world applications, with a particular focus on examples involving shale reservoirs.

Explaining the concept of machine learning, advantages to the industry, and applications applied to complex subsurface rocks, Machine Learning for Subsurface Characterization delivers a missing piece to the reservoir engineer's toolbox needed to support today's complex operations.

• Focus on applying predictive modelling and machine learning from real case studies and Q&A sessions at the end of each chapter
• Learn how to develop codes such as MATLAB, PYTHON, R, and TENSORFLOW with step-by-step guides included
• Visually learn code development with video demonstrations included

Seals and caprocks are an essential component of subsurface hydrogeological systems, guiding the movement and entrapment of hydrocarbon and other fluids. Geological Carbon Storage: Subsurface Seals and Caprock Integrity offers a survey of the wealth of recent scientific work on caprock integrity with a focus on the geological controls of permanent and safe carbon dioxide storage, and the commercial deployment of geological carbon storage.

Volume highlights include:

• Low-permeability rock characterization from the pore scale to the core scale
• Flow and transport properties of low-permeability rocks
• Fundamentals of fracture generation, self-healing, and permeability
• Coupled geochemical, transport and geomechanical processes in caprock
• Analysis of caprock behavior from natural analogues
• Geochemical and geophysical monitoring techniques of caprock failure and integrity
• Potential environmental impacts of carbon dioxide migration on groundwater resources
• Carbon dioxide leakage mitigation and remediation techniques

Geological Carbon Storage: Subsurface Seals and Caprock Integrity is an invaluable resource for geoscientists from academic and research institutions with interests in energy and environment-related problems, as well as professionals in the field.

Book Review: William R. Green, Patrick Taylor, Sven Treitel, and Moritz Fliedner, (2020), "Reviews," The Leading Edge 39: 214-216

Geological Carbon Storage: Subsurface Seals and Caprock Integrity, edited by St phanie Vialle, Jonathan Ajo-Franklin, and J. William Carey, ISBN 978-1-119-11864-0, 2018, American Geophysical Union and Wiley, 364 p., US$199.95 (print), US$159.99 (eBook).

This volume is a part of the AGU/Wiley Geophysical Monograph Series. The editors assembled an international team of earth scientists who present a comprehensive approach to the major problem of placing unwanted and/or hazardous fluids beneath a cap rock seal to be impounded.

The compact and informative preface depicts the nature of cap rocks and the problems that may occur over time or with a change in the formation of the cap rock. I have excerpted a quote from the preface that describes the scope of the volume in a concise and thorough matter. "Caprocks can be defined as a rock that prevents the flow of a given fluid at certain temperature, pressure, and chemical conditions. ... A fundamental understanding of these units and of their evolution over time in the context of subsurface carbon storage is still lacking."

This volume describes the scope of current research being conducted on a global scale, with 31 of the 83 authors working outside of the United States. The studies vary but can be generalized as monitoring techniques for cap rock integrity and the consequence of the loss of that integrity. The preface ends by calling out important problems that remain to be answered. These include imaging cap rocks in situ, detecting subsurface leaks before they reach the surface, and remotely examining the state of the cap rock to avert any problems.

Chapter 3 describes how newer methods are used to classify shale. These advanced techniques reveal previously unknown microscopic properties that complicate classification. This is an example of the more we know, the more we don't know.

Volume highlights include:

• A multi-disciplinary treatment of uncertainty quantification
• Case studies with actual data that will appeal to methodology developers
• A Bayesian evidential learning framework that reduces computation and modeling time

Quantifying Uncertainty in Subsurface Systems is a multidisciplinary volume that brings together five major fields: information science, decision science, geosciences, data science and computer science. It will appeal to both students and practitioners, and be a valuable resource for geoscientists, engineers and applied mathematicians.

Read the Editors' Vox: https: //eos.org/editors-vox/quantifying-uncertainty-about-earths-resources

Reviews, The Leading Edge, SEG, May 2020

The subsurface medium created by geologic processes is not always well understood. The data we collect in an attempt to characterize the subsurface can be incomplete and inaccurate. However, if we understand the uncertainty of our data and the models we generate from them, we can make better decisions regarding the management of subsurface resources. Modeling and managing subsurface resources, and properly characterizing and understanding the uncertainties, requires the integration of a variety of scientific and engineering disciplines.

Five case studies are outlined in the introductory chapter, which are used to demonstrate various methods throughout the book. The second chapter introduces the basic notions in decision analysis. Uncertainty quantification is only relevant within the decision framework used. Models alone do not quantify uncer­tainty, but do allow the determination of key variables that influ­ence models and decisions. Next, an overview of the various data science methods relevant to uncertainty quantification in the subsurface is provided. Sensitivity analysis is then covered, specifi­cally Monte Carlo-based sensitivity analysis. The next three chapters develop the Bayesian approach to uncertainty quantifica­tion, and this is the focus of the book.

All of this is brought together in Chapter 8, which describes a solution regarding quantifying the uncertainties for each of the problems presented in the first chapter. The authors admit that it is not the only solution. No single solution fits all problems of uncertainty quantification. The results in this chapter allow the reader to see the previously described methods applied and how choices influence models and decisions. The final two chapters discuss various software components necessary to implement the strategies presented in the book and challenges faced in the future of uncertainty quantification.

The book uses a number of relevant subsurface problems to explore the various aspects of uncertainty quantification. Understanding uncertainty, and how it affects modeling and decision outcomes, is not always straightforward. However, it is necessary in order to make good, consistent decisions. The book is not an easy read. Some portions require good mathematical understanding of the underlying principles. However, the book is well documented and organized. I would say that is not a good book for a beginner, but it is a good resource for someone to get a grounding to go further into the subject. I appreciate the authors putting together this book on a complex problem that is important to our industry.