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Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also compare the results from an algorithm with others to better equip the readers with different strategies to implement automated workflows for subsurface analysis. Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches will help researchers in academia and professional geoscientists working on the subsurface-related problems (oil and gas, geothermal, carbon sequestration, and seismology) at different scales to understand and appreciate current trends in ML approaches, their applications, advances and limitations, and future potential in geosciences by bringing together several contributions in a single volume. Covers fundamentals of simple machine learning and deep learning algorithms, and physics-based approaches written by practitioners in academia and industry Presents detailed case studies of individual machine learning algorithms and optimal strategies in subsurface characterization around the world Offers an analysis of future trends in machine learning in geosciences
Encyclopedia of Renewable Energy, Sustainability and the Environment, Four Volume Set comprehensively covers all renewable energy resources, including wind, solar, hydro, biomass, geothermal energy, and nuclear power, to name a few. In addition to covering the breadth of renewable energy resources at a fundamental level, this encyclopedia delves into the utilization and ideal applications of each resource and assesses them from environmental, economic, and policy standpoints. This book will serve as an ideal introduction to any renewable energy source for students, while also allowing them to learn about a topic in more depth and explore related topics, all in a single resource. Instructors, researchers, and industry professionals will also benefit from this comprehensive reference. Covers all renewable energy technologies in one comprehensive resource“/li> Details renewable energies’ processes, from production to utilization in a single encyclopedia Organizes topics into concise, consistently formatted chapters, perfect for readers who are new to the field Assesses economic challenges faced to implement each type of renewable energy Addresses the challenges of replacing fossil fuels with renewables and covers the environmental impacts of each renewable energy
This book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It explores the fundamentals of data science and machine learning, and how their advances have disrupted the traditional workflows used in the industry and academia, including geology, geophysics, petrophysics, geomechanics, and geochemistry. It then presents the real-world applications and explains that, while this disruption has affected the top-level executives, geoscientists as well as field operators in the industry and academia, machine learning will ultimately benefit these users. The book is written by a practitioner of machine learning and statistics, keeping geoscientists in mind. It highlights the need to go beyond concepts covered in STAT 101 courses and embrace new computational tools to solve complex problems in geosciences. It also offers practitioners, researchers, and academics insights into how to identify, develop, deploy, and recommend fit-for-purpose machine learning models to solve real-world problems in subsurface geosciences.
Artificial Intelligence for Subsurface Characterization and Monitoring provides an in-depth examination of how deep learning accelerates the process of subsurface characterization and monitoring and provides an end-to-end solution. In recent years, deep learning has been introduced to the geoscience community to overcome some longstanding technical challenges. This book explores some of the most important topics in this discipline to explain the unique capability of deep learning in subsurface characterization for hydrocarbon exploration and production and for energy transition. Readers will discover deep learning methods that can improve the quality and efficiency of many of the key steps in subsurface characterization and monitoring. The text is organized into five parts. The first two parts explore deep learning for data enrichment and well log data, including information extraction from unstructured well reports as well as log data QC and processing. Next is a review of deep learning applied to seismic data and data integration, which also covers intelligent processing for clearer seismic images and rock property inversion and validation. The closing section looks at deep learning in time lapse scenarios, including sparse data reconstruction for reducing the cost of 4D seismic data, time-lapse seismic data repeatability enforcement, and direct property prediction from pre-migration seismic data. Focuses on deep learning applications for geoscience provides a one-stop reference for deep learning applications for geoscience Provides comprehensive examples for state-of-art techniques throughout the subsurface characterization workflow Presented applications come with realistic field dataset examples so that readers can learn what to expect in real-life
A comprehensive handbook on state-of-the-art DAS technology and applications Distributed Acoustic Sensing (DAS) is a technology that records sound and vibration signals along a fiber optic cable. Its advantages of high resolution, continuous, and real-time measurements mean that DAS systems have been rapidly adopted for a range of applications, including hazard mitigation, energy industries, geohydrology, environmental monitoring, and civil engineering. Distributed Acoustic Sensing in Geophysics: Methods and Applications presents experiences from both industry and academia on using DAS in a range of geophysical applications. Volume highlights include: DAS concepts, principles, and measurements Comprehensive review of the historical development of DAS and related technologies DAS applications in hydrocarbon, geothermal, and mining industries DAS applications in seismology DAS applications in environmental and shallow geophysics The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
Fractured rock is the host or foundation for innumerable engineered structures related to energy, water, waste, and transportation. Characterizing, modeling, and monitoring fractured rock sites is critical to the functioning of those infrastructure, as well as to optimizing resource recovery and contaminant management. Characterization, Modeling, Monitoring, and Remediation of Fractured Rock examines the state of practice and state of art in the characterization of fractured rock and the chemical and biological processes related to subsurface contaminant fate and transport. This report examines new developments, knowledge, and approaches to engineering at fractured rock sites since the publication of the 1996 National Research Council report Rock Fractures and Fluid Flow: Contemporary Understanding and Fluid Flow. Fundamental understanding of the physical nature of fractured rock has changed little since 1996, but many new characterization tools have been developed, and there is now greater appreciation for the importance of chemical and biological processes that can occur in the fractured rock environment. The findings of Characterization, Modeling, Monitoring, and Remediation of Fractured Rock can be applied to all types of engineered infrastructure, but especially to engineered repositories for buried or stored waste and to fractured rock sites that have been contaminated as a result of past disposal or other practices. The recommendations of this report are intended to help the practitioner, researcher, and decision maker take a more interdisciplinary approach to engineering in the fractured rock environment. This report describes how existing tools-some only recently developed-can be used to increase the accuracy and reliability of engineering design and management given the interacting forces of nature. With an interdisciplinary approach, it is possible to conceptualize and model the fractured rock environment with acceptable levels of uncertainty and reliability, and to design systems that maximize remediation and long-term performance. Better scientific understanding could inform regulations, policies, and implementation guidelines related to infrastructure development and operations. The recommendations for research and applications to enhance practice of this book make it a valuable resource for students and practitioners in this field.
Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the "big data." Deep learning (DL) is a subset of machine learning that processes "big data" to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface. Learn from 13 practical case studies using field, laboratory, and simulation data Become knowledgeable with data science and analytics terminology relevant to subsurface characterization Learn frameworks, concepts, and methods important for the engineer’s and geoscientist’s toolbox needed to support
Climate change is causing unprecedented damage to our ecosystem. Increasing temperatures, ocean warming and acidification, severe droughts, wildfires, altered precipitation patterns, melting glaciers, rising sea levels and amplification of extreme weather events have direct implications for our food systems. While the impacts of such environmental factors on food security are well known, the effects on food safety receive less attention. The purpose of Climate change: Unpacking the burden on food safety is to identify and attempt to quantify some current and anticipated food safety issues that are associated with climate change. The food safety hazards considered in the publication are foodborne pathogens and parasites, harmful algal blooms, pesticides, mycotoxins and heavy metals with emphasis on methylmercury. There is also, a dedicated section on the benefits of forward-looking approaches such as horizon scanning and foresight, which will not only aid in anticipating future challenges in a shifting global food safety landscape, but also help build resilient food systems that can be continually updated as more knowledge is assimilated. By building a more widespread and better understanding of the consequences climate change has on food safety, it is hoped that this document will aid in fostering stronger international cooperation in making our food safer by reducing the global burden of these concerns.