Quantitative Seismic Interpretation And Machine Learning Applications For Subsurface Characterization And Modeling
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| Author | : Siddharth Misra |
| Publisher | : Gulf Professional Publishing |
| Total Pages | : 442 |
| Release | : 2019-10-12 |
| Genre | : Technology & Engineering |
| ISBN | : 0128177373 |
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
| Author | : Shib Sankar Ganguli |
| Publisher | : Elsevier |
| Total Pages | : 518 |
| Release | : 2023-10-27 |
| Genre | : Science |
| ISBN | : 032399718X |
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
| Author | : Per Avseth |
| Publisher | : Cambridge University Press |
| Total Pages | : 524 |
| Release | : 2010-06-10 |
| Genre | : Science |
| ISBN | : 1107320275 |
Quantitative Seismic Interpretation demonstrates how rock physics can be applied to predict reservoir parameters, such as lithologies and pore fluids, from seismically derived attributes. The authors provide an integrated methodology and practical tools for quantitative interpretation, uncertainty assessment, and characterization of subsurface reservoirs using well-log and seismic data. They illustrate the advantages of these new methodologies, while providing advice about limitations of the methods and traditional pitfalls. This book is aimed at graduate students, academics and industry professionals working in the areas of petroleum geoscience and exploration seismology. It will also interest environmental geophysicists seeking a quantitative subsurface characterization from shallow seismic data. The book includes problem sets and a case-study, for which seismic and well-log data, and MATLAB® codes are provided on a website (http://www.cambridge.org/9780521151351). These resources will allow readers to gain a hands-on understanding of the methodologies.
| Author | : David W. Hyndman |
| Publisher | : John Wiley & Sons |
| Total Pages | : 445 |
| Release | : 2013-04-30 |
| Genre | : Science |
| ISBN | : 1118671805 |
Published by the American Geophysical Union as part of the Geophysical Monograph Series, Volume 171. Groundwater is a critical resource and the PrinciPal source of drinking water for over 1.5 billion people. In 2001, the National Research Council cited as a "grand challenge" our need to understand the processes that control water movement in the subsurface. This volume faces that challenge in terms of data integration between complex, multi-scale hydrologie processes, and their links to other physical, chemical, and biological processes at multiple scales. Subsurface Hydrology: Data Integration for Properties and Processes presents the current state of the science in four aspects: Approaches to hydrologie data integration Data integration for characterization of hydrologie properties Data integration for understanding hydrologie processes Meta-analysis of current interpretations Scientists and researchers in the field, the laboratory, and the classroom will find this work an important resource in advancing our understanding of subsurface water movement.
| Author | : Y. Z. Ma |
| Publisher | : Springer |
| Total Pages | : 646 |
| Release | : 2019-07-15 |
| Genre | : Technology & Engineering |
| ISBN | : 3030178609 |
Earth science is becoming increasingly quantitative in the digital age. Quantification of geoscience and engineering problems underpins many of the applications of big data and artificial intelligence. This book presents quantitative geosciences in three parts. Part 1 presents data analytics using probability, statistical and machine-learning methods. Part 2 covers reservoir characterization using several geoscience disciplines: including geology, geophysics, petrophysics and geostatistics. Part 3 treats reservoir modeling, resource evaluation and uncertainty analysis using integrated geoscience, engineering and geostatistical methods. As the petroleum industry is heading towards operating oil fields digitally, a multidisciplinary skillset is a must for geoscientists who need to use data analytics to resolve inconsistencies in various sources of data, model reservoir properties, evaluate uncertainties, and quantify risk for decision making. This book intends to serve as a bridge for advancing the multidisciplinary integration for digital fields. The goal is to move beyond using quantitative methods individually to an integrated descriptive-quantitative analysis. In big data, everything tells us something, but nothing tells us everything. This book emphasizes the integrated, multidisciplinary solutions for practical problems in resource evaluation and field development.
| Author | : Rebecca Bell |
| Publisher | : Elsevier |
| Total Pages | : 384 |
| Release | : 2022-05-27 |
| Genre | : Science |
| ISBN | : 0128196920 |
Interpreting Subsurface Seismic Data presents recent advances in methodologies for seismic imaging and interpretation across multiple applications in geophysics including exploration, marine geology, and hazards. It provides foundational information for context, as well as focussing on recent advances and future challenges. It offers detailed methodologies for interpreting the increasingly vast quantity of data extracted from seismic volumes. Organized into three parts covering foundational context, case studies, and future considerations, Interpreting Subsurface Seismic Data offers a holistic view of seismic data interpretation to ensure understanding while also applying cutting-edge technologies. This view makes the book valuable to researchers and students in a variety of geoscience disciplines, including geophysics, hydrocarbon exploration, applied geology, and hazards. - Presents advanced seismic detection workflows utilized cutting-edge technologies - Integrates geophysics and geology for a variety of applications, using detailed examples - Provides an overview of recent advances in methodologies related to seismic imaging and interpretation
| Author | : |
| Publisher | : Academic Press |
| Total Pages | : 318 |
| Release | : 2020-09-22 |
| Genre | : Science |
| ISBN | : 0128216840 |
Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured media, signal augmentation to improve the generalization of deep neural networks, deep generator priors for Bayesian seismic inversion, as well as a review on homogenization for seismology, and more. - Provides high-level reviews of the latest innovations in geophysics - Written by recognized experts in the field - Presents an essential publication for researchers in all fields of geophysics
| Author | : Shuvajit Bhattacharya |
| Publisher | : Springer |
| Total Pages | : 170 |
| Release | : 2021-06-07 |
| Genre | : Technology & Engineering |
| ISBN | : 9783030717674 |
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.
| Author | : Matt Hall |
| Publisher | : Agile Libre |
| Total Pages | : 134 |
| Release | : 2012 |
| Genre | : Science |
| ISBN | : 0987959409 |
There is something for every subsurface professional in these fifty-two short essays by more than three dozen petroleum geoscientists. The roster includes some of the most prolific geophysicists of our time, as well as some recently qualified scientists. The topics are even more diverse, ranging from anisotropic media to pre-stack interpretation, and from stories of early seismic workstations to career advice for the future.
| Author | : Srikanta Mishra |
| Publisher | : CRC Press |
| Total Pages | : 379 |
| Release | : 2022-12-27 |
| Genre | : Technology & Engineering |
| ISBN | : 1000823873 |
The utilization of machine learning (ML) techniques to understand hidden patterns and build data-driven predictive models from complex multivariate datasets is rapidly increasing in many applied science and engineering disciplines, including geo-energy. Motivated by these developments, Machine Learning Applications in Subsurface Energy Resource Management presents a current snapshot of the state of the art and future outlook for ML applications to manage subsurface energy resources (e.g., oil and gas, geologic carbon sequestration, and geothermal energy). Covers ML applications across multiple application domains (reservoir characterization, drilling, production, reservoir modeling, and predictive maintenance) Offers a variety of perspectives from authors representing operating companies, universities, and research organizations Provides an array of case studies illustrating the latest applications of several ML techniques Includes a literature review and future outlook for each application domain This book is targeted at practicing petroleum engineers or geoscientists interested in developing a broad understanding of ML applications across several subsurface domains. It is also aimed as a supplementary reading for graduate-level courses and will also appeal to professionals and researchers working with hydrogeology and nuclear waste disposal.
