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Author: Aria Abubakar
Publisher: Elsevier
ISBN: 0443224226
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
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
Author: Aria Abubakar
Publisher: Elsevier
ISBN: 0443224226
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
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
Author: Committee for Noninvasive Characterization of the Shallow Subsurface for Environmental and Engineering Applications
Publisher: National Academies Press
ISBN: 0309521513
Category : Science
Languages : en
Pages : 156
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Book Description
Just below our feet is an environment that supports our infrastructure, yields water, provides for agriculture, and receives our waste. Our capacity to describe, or characterize, this environment is crucial to the solution of many resource, environmental, and engineering problems. And just as medical imaging technologies have reduced the need for exploratory surgeries, a variety of technologies hold the promise for rapid, relatively inexpensive noninvasive characterization of the Earth's subsurface. Seeing into the Earth examines why noninvasive characterization is important and how improved methods can be developed and disseminated. Looking at the issues from both the commercial and public perspectives, the volume makes recommendations for linking characterization and cost savings, closing the gap between the state of science and the state of the practice, and helping practitioners make the best use of the best methods. The book provides background on: The role of noninvasive subsurface characterization in contaminant cleanup, resource management, civil engineering, and other areas. The physical, chemical, biological, and geological properties that are characterized. Methods of characterization and prospects for technological improvement. Certain to be important for earth scientists and engineers alike, this book is also accessible to interested lay readers.
Author: Shuvajit Bhattacharya
Publisher: Springer Nature
ISBN: 3030717682
Category : Technology & Engineering
Languages : en
Pages : 172
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Book Description
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: Shuvajit Bhattacharya
Publisher: Elsevier
ISBN: 0128223081
Category : Computers
Languages : en
Pages : 378
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Book Description
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
Author: Frank Kirchner
Publisher: Springer Nature
ISBN: 3030306836
Category : Technology & Engineering
Languages : en
Pages : 193
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Book Description
This book provides exclusive insight into the development of a new generation of robotic underwater technologies. Deploying and using even the most simple and robust mechanical tools is presenting a challenge, and is often associated with an enormous amount of preparation, continuous monitoring, and maintenance. Therefore, all disciplinary aspects (e.g. system design, communication, machine learning, mapping and coordination, adaptive mission planning) are examined in detail and together this gives an extensive overview on research areas influencing next generation underwater robots. These robotic underwater systems will operate autonomously with the help of the most modern artificial intelligence procedures and perform environmental monitoring as well as inspection and maintenance of underwater structures. The systems are designed as modular and reconfigurable systems for long term autonomy to remain at the site for longer periods of time. New communication methods using AI enable missions of hybrid teams of humans and heterogeneous robots. Thus this volume will be an important reference for scientists on every qualification level in the field of underwater technologies, industrial maritime applications, and maritime science.
Author: Hui Wang
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 51
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Book Description
Due to the inadequate knowledge of the soil forming histories and/or human activities, the subsurface soil layers are difficult to ascertain. Subsurface uncertainty and its influence on geotechnical design have long been a challenge facing practitioners. Recently, the ASCE Geo-institute has developed the Data Interchange for Geotechnical and Geoenvironmental Specialists (DIGGS), which is a standard schema for transferring geotechnical data between multiple organizations. It paves the way of sharing and unifying datasets and forms a structural database for further data-driven modeling and analysis. The Office of Geotechnical Engineering at ODOT (OGE) is taking a national leading role in supporting the development efforts of DIGGS and hence make this project possible. In this study, site investigation data in DIGGS format and archived format are jointly processed. An innovative technique developed by the research team has been further improved for better application in real-world projects. Bayesian machine learning is integrated with Markov random field models to infer and simulate subsurface models and geospatial data with quantified uncertainty. Spatial heterogeneity and statistical characteristics are modeled in terms of statistical and spatial patterns. These patterns serve as a basis to provide a synthesized interpretation of the soil profiles with uncertainty quantified. Four (4) validation projects have been performed in this report and the results are well documented. Summary and recommendations for future work are also provided. A short introduction of the key concepts behind this technique, and pathway for converting the existing program into a ready for implementation web-based program for potential ODOT usages are provided in the appendices.
Author: National Research Council
Publisher:
ISBN: 9780309063593
Category :
Languages : en
Pages : 0
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Book Description
Author: Weitao Chen
Publisher: Springer
ISBN: 9789819958214
Category : Nature
Languages : en
Pages : 0
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Book Description
This book comprehensively utilizes the new generation of artificial intelligence and remote sensing science and technology to systematically carry out researches on high-precision recognition, monitoring, analysis, and assessment of geological disasters by using different technologies of "ground, airspace, and space-based systems" and different scales of "target-semantic-region". The main contents include: 1) Intelligent interpretation theory and methods of geological disasters, 2) Intelligent analysis of landslide based on long-term ground monitoring data, 3) Deep learning-based remote sensing detection of landslide, 4) Intelligent analysis of landslide evolution based on optical satellite remote sensing data, 5) Intelligent assessment methods of landslide susceptibility, 6) Intelligent recognition of ground figure based on airspace-based remote sensing data, 7) Intelligent monitoring of land subsidence in mining areas based on InSAR technology. The book is of interest to graduate student, scientific, and technological personnel who work in the area of geological disasters, natural hazards, remote sensing, and artificial intelligence.
Author: J.M. Miocic
Publisher: Geological Society of London
ISBN: 1786205769
Category : Science
Languages : en
Pages : 507
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Book Description
The secure storage of energy and carbon dioxide in subsurface geological formations plays a crucial role in transitioning to a low-carbon energy system. The suitability and security of subsurface storage sites rely on the geological and hydraulic properties of the reservoir and confining units. Additionally, their ability to withstand varying thermal, mechanical, hydraulic, biological and chemical conditions during storage operations is essential. Each subsurface storage technology has distinct geological requirements and faces specific economic, logistical, public and scientific challenges. As a result, certain sites can be better suited than others for specific low-carbon energy applications. This Special Publication provides a summary of the state of the art in subsurface energy and carbon dioxide storage. It includes 20 case studies that offer insights into site selection, characterization of reservoir processes, the role of caprocks and fault seals, as well as monitoring and risk assessment needs for subsurface storage operations.
Author: Yunhui Zhang
Publisher: Frontiers Media SA
ISBN: 2832529925
Category : Science
Languages : en
Pages : 291
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Book Description
