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Author: Aria Abubakar Publisher: Elsevier ISBN: 9780443235177 Category : Technology & Engineering Languages : en Pages : 0
Book Description
Artificial Intelligence for Subsurface Characterization and Monitoring provides an in-depth examination of how deep learning accelerates the process of subsurface characterization & monitoring and provides an end-to-end solution. In recent years, deep learning has been introduced to the geoscience community to overcome some long-standing 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. 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. Readers will discover deep learning methods that can improve the quality and efficiency of many of the key steps in subsurface characterization and monitoring.
Author: Aria Abubakar Publisher: Elsevier ISBN: 9780443235177 Category : Technology & Engineering Languages : en Pages : 0
Book Description
Artificial Intelligence for Subsurface Characterization and Monitoring provides an in-depth examination of how deep learning accelerates the process of subsurface characterization & monitoring and provides an end-to-end solution. In recent years, deep learning has been introduced to the geoscience community to overcome some long-standing 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. 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. Readers will discover deep learning methods that can improve the quality and efficiency of many of the key steps in subsurface characterization and monitoring.
Author: J.M. Miocic Publisher: Geological Society of London ISBN: 1786205769 Category : Science Languages : en Pages : 507
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: Srikanta Mishra Publisher: CRC Press ISBN: 100082389X Category : Technology & Engineering Languages : en Pages : 388
Book Description
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.
Author: M. Z. Naser Publisher: Elsevier ISBN: 0128240741 Category : Technology & Engineering Languages : en Pages : 300
Book Description
The past few years have demonstrated how civil infrastructure continues to experience an unprecedented scale of extreme loading conditions (i.e. hurricanes, wildfires and earthquakes). Despite recent advancements in various civil engineering disciplines, specific to the analysis, design and assessment of structures, it is unfortunate that it is common nowadays to witness large scale damage in buildings, bridges and other infrastructure. The analysis, design and assessment of infrastructure comprises of a multitude of dimensions spanning a highly complex paradigm across material sciences, structural engineering, construction and planning among others. While traditional methods fall short of adequately accounting for such complexity, fortunately, computational intelligence presents novel solutions that can effectively tackle growing demands of intense extreme events and modern designs of infrastructure – especially in this era where infrastructure is reaching new heights and serving larger populations with high social awareness and expectations. Computational Intelligence for Analysis, Design and Assessment of Civil Infrastructure highlights the growing trend of fostering the use of CI to realize contemporary, smart and safe infrastructure. This is an emerging area that has not fully matured yet and hence the book will draw considerable interest and attention. In a sense, the book presents results of innovative efforts supplemented with case studies from leading researchers that can be used as benchmarks to carryout future experiments and/or facilitate development of future experiments and advanced numerical models. The book is written with the intention to serve as a guide for a wide audience including senior postgraduate students, academic and industrial researchers, materials scientists and practicing engineers working in civil, structural and mechanical engineering. Focuses on civil engineering applications for extreme events Explains the fundamentals of AI/ML and how they can be applied in civil engineering Features case study examples, design codes, and problems and solutions that would work for extreme events
Author: Radek Silhavy Publisher: Springer Nature ISBN: 3031090764 Category : Technology & Engineering Languages : en Pages : 627
Book Description
This book covers themes related to artificial intelligence in systems and networks application. Selected papers explore modern neural networks application, optimization and hybrid and bio-inspired algorithms are covered too. The refereed proceedings of the Artificial Intelligence Trends in Systems part of the 11th Computer Science On-line Conference 2022 (CSOC 2022), conducted online in April 2022, are included in this volume.
Author: Manan Shah Publisher: CRC Press ISBN: 1000629554 Category : Technology & Engineering Languages : en Pages : 162
Book Description
Today, raw data on any industry is widely available. With the help of artificial intelligence (AI) and machine learning (ML), this data can be used to gain meaningful insights. In addition, as data is the new raw material for today’s world, AI and ML will be applied in every industrial sector. Industry 4.0 mainly focuses on the automation of things. From that perspective, the oil and gas industry is one of the largest industries in terms of economy and energy. Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry analyzes the use of AI and ML in the oil and gas industry across all three sectors, namely upstream, midstream, and downstream. It covers every aspect of the petroleum industry as related to the application of AI and ML, ranging from exploration, data management, extraction, processing, real-time data analysis, monitoring, cloud-based connectivity system, and conditions analysis, to the final delivery of the product to the end customer, while taking into account the incorporation of the safety measures for a better operation and the efficient and effective execution of operations. This book explores the variety of applications that can be integrated to support the existing petroleum and adjacent sectors to solve industry problems. It will serve as a useful guide for professionals working in the petroleum industry, industrial engineers, AI and ML experts and researchers, as well as students.
Author: Srikanta Mishra Publisher: CRC Press ISBN: 1000823873 Category : Technology & Engineering Languages : en Pages : 379
Book Description
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.
Author: Daniel Asante Otchere Publisher: CRC Press ISBN: 1003860192 Category : Science Languages : en Pages : 322
Book Description
This book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems. Processing voluminous and complex data sets are the primary focus of the field of machine learning (ML). ML aims to develop data-driven methods and computational algorithms that can learn to identify complex and non-linear patterns to understand and predict the relationships between variables by analysing extensive data. Although ML models provide the final output for predictions, several steps need to be performed to achieve accurate predictions. These steps, data pre-processing, feature selection, feature engineering and outlier removal, are all contained in this book. New models are also developed using existing ML architecture and learning theories to improve the performance of traditional ML models and handle small and big data without manual adjustments. This research-oriented book will help subsurface engineers, geophysicists, and geoscientists become familiar with data science and ML advances relevant to subsurface engineering. Additionally, it demonstrates the use of data-driven approaches for salt identification, seismic interpretation, estimating enhanced oil recovery factor, predicting pore fluid types, petrophysical property prediction, estimating pressure drop in pipelines, bubble point pressure prediction, enhancing drilling mud loss, smart well completion and synthetic well log predictions.
Author: Maria Pia Del Rosso Publisher: IET ISBN: 1839532122 Category : Computers Languages : en Pages : 283
Book Description
This book shows how artificial intelligence, including neural networks and deep learning, can be applied to the processing of satellite data for Earth observation. The authors explain how to develop a set of libraries for the implementation of artificial intelligence that encompass different aspects of research.
Author: Aria Abubakar
Author: Aria Abubakar
Author: J.M. Miocic
Author: Yunhui Zhang
Author: Srikanta Mishra
Author: M. Z. Naser
Author: Radek Silhavy
Author: Manan Shah
Author: Srikanta Mishra
Author: Daniel Asante Otchere
Author: Maria Pia Del Rosso