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Author: Elias Augusto Machado Roberty
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The oil and gas industry faces profitability and sustainability challenges that demand companies to have more efficient, reliable, safe, and environmentally friendly operations. Furthermore, as oil and gas companies embark on the Industry 4.0 journey, the pillar of big data becomes increasingly important in an industry that generates massive amounts of data with low to no value extracted from it. Data are generated across all value chain sectors--upstream, midstream, and downstream--starting at reservoirs up to the finished products delivered by the refining and petrochemical sectors. Processing facilities across the value chain, where physical and chemical unit operations convert raw products into intermediate and finished products, generate a wealth of data through their heavily instrumented automatic control systems, operational routines, and quality control systems. Analyzing process data can help companies develop models that predict key process-related parameters to correct potential process upsets timely. In addition, predictive models can also be incorporated into digital twins to emulate diverse operating scenarios for production optimization or facility design purposes. This thesis investigates and reviews the application of predictive analytics on process data, its potential untapped value, analytics as an enabler of digital twins, and big data analytics frameworks tailored for an oil and gas context. Use cases across all segments of the value chain are reviewed with their respective predictive methods. The value of predictive analytics in oil and gas is assessed by reviewing various sources, including a major oil company success case followed by the architectural integration of predictive analytics into the development of a digital twin employing a systems-oriented approach. The last chapter discusses the predictive component of a novel approach tailored for process data analytics: Smart Process Analytics. The advantages such a framework offers versus standard automated predictive model development processes are discussed. Lastly, big data architectures for SPA implementation at process plants are developed.
Author: Elias Augusto Machado Roberty
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The oil and gas industry faces profitability and sustainability challenges that demand companies to have more efficient, reliable, safe, and environmentally friendly operations. Furthermore, as oil and gas companies embark on the Industry 4.0 journey, the pillar of big data becomes increasingly important in an industry that generates massive amounts of data with low to no value extracted from it. Data are generated across all value chain sectors--upstream, midstream, and downstream--starting at reservoirs up to the finished products delivered by the refining and petrochemical sectors. Processing facilities across the value chain, where physical and chemical unit operations convert raw products into intermediate and finished products, generate a wealth of data through their heavily instrumented automatic control systems, operational routines, and quality control systems. Analyzing process data can help companies develop models that predict key process-related parameters to correct potential process upsets timely. In addition, predictive models can also be incorporated into digital twins to emulate diverse operating scenarios for production optimization or facility design purposes. This thesis investigates and reviews the application of predictive analytics on process data, its potential untapped value, analytics as an enabler of digital twins, and big data analytics frameworks tailored for an oil and gas context. Use cases across all segments of the value chain are reviewed with their respective predictive methods. The value of predictive analytics in oil and gas is assessed by reviewing various sources, including a major oil company success case followed by the architectural integration of predictive analytics into the development of a digital twin employing a systems-oriented approach. The last chapter discusses the predictive component of a novel approach tailored for process data analytics: Smart Process Analytics. The advantages such a framework offers versus standard automated predictive model development processes are discussed. Lastly, big data architectures for SPA implementation at process plants are developed.
Author: Shahab D. Mohaghegh
Publisher: Springer
ISBN: 3319487531
Category : Technology & Engineering
Languages : en
Pages : 292
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Book Description
This book describes the application of modern information technology to reservoir modeling and well management in shale. While covering Shale Analytics, it focuses on reservoir modeling and production management of shale plays, since conventional reservoir and production modeling techniques do not perform well in this environment. Topics covered include tools for analysis, predictive modeling and optimization of production from shale in the presence of massive multi-cluster, multi-stage hydraulic fractures. Given the fact that the physics of storage and fluid flow in shale are not well-understood and well-defined, Shale Analytics avoids making simplifying assumptions and concentrates on facts (Hard Data - Field Measurements) to reach conclusions. Also discussed are important insights into understanding completion practices and re-frac candidate selection and design. The flexibility and power of the technique is demonstrated in numerous real-world situations.
Author: Patrick Bangert
Publisher: Gulf Professional Publishing
ISBN: 0128209143
Category : Science
Languages : en
Pages : 290
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Book Description
Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful Gain practical understanding of machine learning used in oil and gas operations through contributed case studies Learn change management skills that will help gain confidence in pursuing the technology Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)
Author: Sathish Sankaran
Publisher:
ISBN: 9781613998205
Category :
Languages : en
Pages : 108
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Book Description
Data Analytics in Reservoir Engineering describes the relevance of data analytics for the oil and gas industry, with particular emphasis on reservoir engineering.
Author: Razin Farhan Hussain
Publisher: Gulf Professional Publishing
ISBN: 0323998445
Category : Science
Languages : en
Pages : 268
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Book Description
IoT for Smart Operations in the Oil and Gas Industry elaborates on how the synergy between state-of-the-art computing platforms, such as Internet of Things (IOT), cloud computing, artificial intelligence, and, in particular, modern machine learning methods, can be harnessed to serve the purpose of a more efficient oil and gas industry. The reference explores the operations performed in each sector of the industry and then introduces the computing platforms and smart technologies that can enhance the operation, lower costs, and lower carbon footprint. Safety and security content is included, in particular, cybersecurity and potential threats to smart oil and gas solutions, focusing on adversarial effects of smart solutions and problems related to the interoperability of human-machine intelligence in the context of the oil and gas industry. Detailed case studies are included throughout to learn and research for further applications. Covering the latest topics and solutions, IoT for Smart Operations in the Oil and Gas Industry delivers a much-needed reference for the engineers and managers to understand modern computing paradigms for Industry 4.0 and the oil and gas industry. Follows a systematic and categorical taxonomy of the upstream, midstream, and downstream processes paired with cutting-edge technologies, which benefit computer scientists and engineers Understands advanced computing technologies reducing the costs of existing operations and carbon footprint Deeply dives into case studies that cover the entire oil and gas spectrum and explain bridges into applications
Author: Jay Gohil
Publisher: CRC Press
ISBN: 1000580024
Category : Computers
Languages : en
Pages : 171
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Book Description
The book aims to provide comprehensive knowledge and information pertaining to application or implementation of big data in the petroleum industry and its operations (such as exploration, production, refining and finance). The book covers intricate aspects of big data such as 6Vs, benefits, applications, implementation, research work and real-world implementation pertaining to each petroleum-associated operation in a concise manner that aids the reader to apprehend the overview of big data’s role in the industry. The book resonates with readers who wish to understand the intricate details of working with big data (along with data science, machine learning and artificial intelligence) in general and how it affects and impacts an entire industry. As the book builds various concepts of big data from scratch to industry level, readers who wish to gain big data-associated knowledge of industry level in simple language from the very fundamentals would find this a wonderful read.
Author: Pethuru Raj Chelliah
Publisher: John Wiley & Sons
ISBN: 1119985609
Category : Computers
Languages : en
Pages : 516
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Book Description
The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry Comprehensive resource describing how operations, outputs, and offerings of the oil and gas industry can improve via advancements in AI The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry describes the proven and promising digital technologies and tools available to empower the oil and gas industry to be future-ready. It shows how the widely reported limitations of the oil and gas industry are being nullified through the application of breakthrough digital technologies and how the convergence of digital technologies helps create new possibilities and opportunities to take this industry to its next level. The text demonstrates how scores of proven digital technologies, especially in AI, are useful in elegantly fulfilling complicated requirements such as process optimization, automation and orchestration, real-time data analytics, productivity improvement, employee safety, predictive maintenance, yield prediction, and accurate asset management for the oil and gas industry. The text differentiates and delivers sophisticated use cases for the various stakeholders, providing easy-to-understand information to accurately utilize proven technologies towards achieving real and sustainable industry transformation. The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry includes information on: How various machine and deep learning (ML/DL) algorithms, the prime modules of AI, empower AI systems to deliver on their promises and potential Key use cases of computer vision (CV) and natural language processing (NLP) as they relate to the oil and gas industry Smart leverage of AI, the Industrial Internet of Things (IIoT), cyber physical systems, and 5G communication Event-driven architecture (EDA), microservices architecture (MSA), blockchain for data and device security, and digital twins Clearly expounding how the power of AI and other allied technologies can be meticulously leveraged by the oil and gas industry, The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry is an essential resource for students, scholars, IT professionals, and business leaders in many different intersecting fields.
Author: P. Dowey
Publisher: Geological Society of London
ISBN: 1786204061
Category : Science
Languages : en
Pages : 345
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Book Description
Cutting-edge techniques have always been utilized in petroleum exploration and production to reduce costs and improve efficiencies. The demand for petroleum in the form of oil and gas is expected to increase for electricity production, transport and chemical production, largely driven by an increase in energy consumption in the developing world. Innovations in analytical methods will continue to play a key role in the industry moving forwards as society shifts towards lower carbon energy systems and more advantaged oil and gas resources are targeted. This volume brings together new analytical approaches and describes how they can be applied to the study of petroleum systems. The papers within this volume cover a wide range of topics and case studies, in the fields of fluid and isotope geochemistry, organic geochemistry, imaging and sediment provenance. The work illustrates how the current, state-of-the-art technology can be effectively utilised to address ongoing challenges in petroleum geoscience.
Author: Gustavo Carvajal
Publisher: Gulf Professional Publishing
ISBN: 012804747X
Category : Technology & Engineering
Languages : en
Pages : 376
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Book Description
Intelligent Digital Oil and Gas Fields: Concepts, Collaboration, and Right-time Decisions delivers to the reader a roadmap through the fast-paced changes in the digital oil field landscape of technology in the form of new sensors, well mechanics such as downhole valves, data analytics and models for dealing with a barrage of data, and changes in the way professionals collaborate on decisions. The book introduces the new age of digital oil and gas technology and process components and provides a backdrop to the value and experience industry has achieved from these in the last few years. The book then takes the reader on a journey first at a well level through instrumentation and measurement for real-time data acquisition, and then provides practical information on analytics on the real-time data. Artificial intelligence techniques provide insights from the data. The road then travels to the "integrated asset" by detailing how companies utilize Integrated Asset Models to manage assets (reservoirs) within DOF context. From model to practice, new ways to operate smart wells enable optimizing the asset. Intelligent Digital Oil and Gas Fields is packed with examples and lessons learned from various case studies and provides extensive references for further reading and a final chapter on the "next generation digital oil field," e.g., cloud computing, big data analytics and advances in nanotechnology. This book is a reference that can help managers, engineers, operations, and IT experts understand specifics on how to filter data to create useful information, address analytics, and link workflows across the production value chain enabling teams to make better decisions with a higher degree of certainty and reduced risk. Covers multiple examples and lessons learned from a variety of reservoirs from around the world and production situations Includes techniques on change management and collaboration Delivers real and readily applicable knowledge on technical equipment, workflows and data challenges such as acquisition and quality control that make up the digital oil and gas field solutions of today Describes collaborative systems and ways of working and how companies are transitioning work force to use the technology and making more optimal decisions
Author: Rasool Khosravanian
Publisher: Gulf Professional Publishing
ISBN: 0323902324
Category : Science
Languages : en
Pages : 554
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Book Description
Drilling and production wells are becoming more digitalized as oil and gas companies continue to implement machine learning andbig data solutions to save money on projects while reducing energy and emissions. Up to now there has not been one cohesiveresource that bridges the gap between theory and application, showing how to go from computer modeling to practical use. Methodsfor Petroleum Well Optimization: Automation and Data Solutions gives today’s engineers and researchers real-time data solutionsspecific to drilling and production assets. Structured for training, this reference covers key concepts and detailed approaches frommathematical to real-time data solutions through technological advances. Topics include digital well planning and construction,moving teams into Onshore Collaboration Centers, operations with the best machine learning (ML) and metaheuristic algorithms,complex trajectories for wellbore stability, real-time predictive analytics by data mining, optimum decision-making, and case-basedreasoning. Supported by practical case studies, and with references including links to open-source code and fit-for-use MATLAB, R,Julia, Python and other standard programming languages, Methods for Petroleum Well Optimization delivers a critical training guidefor researchers and oil and gas engineers to take scientifically based approaches to solving real field problems. Bridges the gap between theory and practice (from models to code) with content from the latest research developments supported by practical case study examples and questions at the end of each chapter Enables understanding of real-time data solutions and automation methods available specific to drilling and production wells, suchas digital well planning and construction through to automatic systems Promotes the use of open-source code which will help companies, engineers, and researchers develop their prediction and analysissoftware more quickly; this is especially appropriate in the application of multivariate techniques to the real-world problems of petroleum well optimization
