Download or read book Rate Transient Analysis Via Artificial Intelligence written by Lu Jia and published by LAP Lambert Academic Publishing. This book was released on 2015-06-23 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this research is to develop a mathematical model as a reservoir estimation tool for naturally fractured reservoirs with dual lateral well configurations. The tool proposed in this study includes a forward artificial neural network (ANN) with the ability to predict production data via known reservoir and well design parameters. The proposed tool also includes an inverse ANN component that can be used to predict the permeability and porosity of matrix and fracture, as well as fracture spacing and reservoir thickness. By means of the proposed tool, the user would be able to analyze instantaneously predicted reservoir or production data with less cost and time. The software involved in developing the tool were MATLAB, EXCEL, and a commercial modeling software. The procedures are introduced and discussed in the following chapters including training data generation, selecting training data sets, training forward and inverse ANN models. Moreover, a graphical user interface (GUI) is developed and assembled for each ANN, which allows the user to view results in numerical and graphical formats.
Download or read book Rate Transient Analysis Of Dual Lateral Wells In Naturally Fractured Reservoirs Via Artificial Intelligence written by Jia Lu and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In naturally fractured reservoirs, reservoir characterization is critical to the production of hydrocarbon, including but not limited to porosity, permeability, pay zone thickness and fracture spacing. Laboratory measurements, well-logging technique, and mathematical models are three major characterization approaches that are widely used to determine and analyze the reservoir characterization and production profiles. Amongst these approaches, mathematical models are commonly used as estimation tools. The purpose of this thesis is to develop a mathematical model as a reservoir estimation tool for naturally fractured reservoirs with dual lateral well configurations. The tool proposed in this study includes a forward artificial neural network (ANN) with the ability to predict production data via known reservoir and well design parameters. The proposed tool also includes an inverse ANN component that can be used to predict the permeability and porosity of matrix and fracture, as well as fracture spacing and reservoir thickness. By means of the proposed tool, the user would be able to analyze instantaneously predicted reservoir or production data with less cost and time. The software involved in developing the tool were MATLAB, EXCEL, and a commercial modeling software1. The procedures are introduced and discussed in the following chapters including training data generation, selecting training data sets, training forward and inverse ANN models. Moreover, a graphical user interface (GUI) is developed and assembled for each ANN, which allows the user to view results in numerical and graphical formats.
Download or read book Applications of Inverse Theory and Machine Learning in Rate pressure Transient Analysis written by Nitinkumar Lalitkumar Chaudhary and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The applicability of decline relations (empirical or analytical) in rate transient analysis (RTA) to forecast the production of an unconventional reservoir depends on the validity of assumptions made during the development of these decline relations. None of the present rate decline relations used to estimate the ultimate recovery (EUR), are conceptually applicable in a reservoir where hydrocarbon production exhibits variable rate and pressure. We propose a new methodology based on an inverse deconvolution problem formulation to accurately forecast performance in such reservoirs. To handle the instability in deconvolution, we propose the use of elastic net regularization and a new weighting scheme in our deconvolution algorithm. In this work, we propose a non-parametric density-based outlier detection method, which identifies outliers by classifying the data into clusters and assigning local outlier factors to the individual data points. We validate our method using synthetic examples generated using numerical models of multi-stage hydraulically fractured wells in unconventional reservoirs. Upon validation we demonstrate our method using field examples. Our work demonstrates that this new methodology integrating, pressures into decline curve analysis is theoretically and practically more robust than the analysis of pressure normalized decline curves currently used to solve the problem.
Download or read book Artificial Intelligence for Science and Engineering Applications written by Shahab D. Mohaghegh and published by CRC Press. This book was released on 2024-04-01 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) is defined as the simulation of human intelligence through the mimicking of the human brain for analysis, modeling, and decision‐making. Science and engineering problem solving requires modeling of physical phenomena, and humans approach the solution of scientific and engineering problems differently from other problems. Artificial Intelligence for Science and Engineering Applications addresses the unique differences in how AI should be developed and used in science and engineering. Through the inclusion of definitions and detailed examples, this book describes the actual and realistic requirements as well as what characteristics must be avoided for correct and successful science and engineering applications of AI. This book: Offers a brief history of AI and covers science and engineering applications Explores the modeling of physical phenomena using AI Discusses explainable AI (XAI) applications Covers the ethics of AI in science and engineering Features real‐world case studies Offering a probing view into the unique nature of scientific and engineering exploration, this book will be of interest to generalists and experts looking to expand their understanding of how AI can better tackle and advance technology and developments in scientific and engineering disciplines.
Download or read book Artificial Intelligence and Data Analytics for Energy Exploration and Production written by Fred Aminzadeh and published by John Wiley & Sons. This book was released on 2022-08-26 with total page 613 pages. Available in PDF, EPUB and Kindle. Book excerpt: ARTIFICAL INTELLIGENCE AND DATA ANALYTICS FOR ENERGY EXPLORATION AND PRODUCTION This groundbreaking new book is written by some of the foremost authorities on the application of data science and artificial intelligence techniques in exploration and production in the energy industry, covering the most comprehensive and updated new processes, concepts, and practical applications in the field. The book provides an in-depth treatment of the foundations of Artificial Intelligence (AI) Machine Learning, and Data Analytics (DA). It also includes many of AI-DA applications in oil and gas reservoirs exploration, development, and production. The book covers the basic technical details on many tools used in “smart oil fields”. This includes topics such as pattern recognition, neural networks, fuzzy logic, evolutionary computing, expert systems, artificial intelligence machine learning, human-computer interface, natural language processing, data analytics and next-generation visualization. While theoretical details will be kept to the minimum, these topics are introduced from oil and gas applications viewpoints. In this volume, many case histories from the recent applications of intelligent data to a number of different oil and gas problems are highlighted. The applications cover a wide spectrum of practical problems from exploration to drilling and field development to production optimization, artificial lift, and secondary recovery. Also, the authors demonstrate the effectiveness of intelligent data analysis methods in dealing with many oil and gas problems requiring combining machine and human intelligence as well as dealing with linguistic and imprecise data and rules.
Download or read book Machine Learning Guide for Oil and Gas Using Python written by Hoss Belyadi and published by Gulf Professional Publishing. This book was released on 2021-04-09 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges. - Helps readers understand how open-source Python can be utilized in practical oil and gas challenges - Covers the most commonly used algorithms for both supervised and unsupervised learning - Presents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques
Download or read book Proceedings of the International Field Exploration and Development Conference 2023 written by Jia'en Lin and published by Springer Nature. This book was released on with total page 965 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Sustainable Natural Gas Reservoir and Production Engineering written by David Wood and published by Gulf Professional Publishing. This book was released on 2021-10-30 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sustainable Natural Gas Reservoir and Production Engineering, the latest release in The Fundamentals and Sustainable Advances in Natural Gas Science and Engineering series, delivers many of the scientific fundamentals needed in the natural gas industry, including improving gas recovery, simulation processes for fracturing methods, and methods for optimizing production strategies. Advanced research covered includes machine learning applications, gas fracturing mechanics aimed at reducing environmental impact, and enhanced oil recovery technologies aimed at capturing carbon dioxide. Supported by corporate and academic contributors along with two well-distinguished editors, this book provides today's natural gas engineers the fundamentals and advances in a convenient resource - Helps readers advance from basic equations used in conventional gas reservoirs - Presents structured case studies to illustrate how new principles can be applied in practical situations - Covers advanced topics, including machine learning applications to optimize predictions, controls and improve knowledge-based applications - Helps accelerate emission reductions by teaching gas fracturing mechanics with an aim of reducing environmental impacts and developing enhanced oil recovery technologies that capture carbon dioxide
Download or read book Industrial And Engineering Applications Of Artificial Intelligence And Expert Systems written by Moonis Ali and published by CRC Press. This book was released on 1988-08 with total page 636 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Rate Transient Analysis written by Fekete Associates Inc and published by . This book was released on 2008 with total page 1 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Shale Analytics written by Shahab D. Mohaghegh and published by Springer. This book was released on 2017-02-09 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Artificial Intelligence and Other Innovative Computer Applications in the Nuclear Industry written by M. Catherine Majumdar and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 866 pages. Available in PDF, EPUB and Kindle. Book excerpt: This conference brought together experts from 15 countries to discuss application of Artificial Intelligence (AI) techniques to the nuclear industry. It was apparent from the meeting that even those active in the field were surprised at the extent of work and the progress made. There was a strong impression that application of this technology to nuclear power plants is inevitable. The benefits to improved operation, design, and safety are simply too significant to be ignored. This is a much different conclusion than might have been reached a few years ago when the technology was new and people were struggling to understand its significance. We believe that this meeting reflects a major turning point for the technology. It has moved from being a topic understood only by specialists to a situation where users are the most active people in the field. A broad array of innovative work is described from all of the participating countries. The activity in the u.s. is large and diverse. Although there is no nationally focussed policy for AI research in the U.S., many of these activities are reported here. Japan and France have a strong drive to integrate AI technology into their nuclear plants, and this is reflecteq in these proceedings.
Download or read book Machine Learning Applications in Subsurface Energy Resource Management written by Srikanta Mishra and published by CRC Press. This book was released on 2022-12-27 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Hydraulic Fracturing in Unconventional Reservoirs written by Hoss Belyadi and published by Gulf Professional Publishing. This book was released on 2019-06-18 with total page 636 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hydraulic Fracturing in Unconventional Reservoirs: Theories, Operations, and Economic Analysis, Second Edition, presents the latest operations and applications in all facets of fracturing. Enhanced to include today's newest technologies, such as machine learning and the monitoring of field performance using pressure and rate transient analysis, this reference gives engineers the full spectrum of information needed to run unconventional field developments. Covering key aspects, including fracture clean-up, expanded material on refracturing, and a discussion on economic analysis in unconventional reservoirs, this book keeps today's petroleum engineers updated on the critical aspects of unconventional activity. - Helps readers understand drilling and production technology and operations in shale gas through real-field examples - Covers various topics on fractured wells and the exploitation of unconventional hydrocarbons in one complete reference - Presents the latest operations and applications in all facets of fracturing
Download or read book Artificial Intelligence Techniques for a Scalable Energy Transition written by Moamar Sayed-Mouchaweh and published by Springer Nature. This book was released on 2020-06-19 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents research in artificial techniques using intelligence for energy transition, outlining several applications including production systems, energy production, energy distribution, energy management, renewable energy production, cyber security, industry 4.0 and internet of things etc. The book goes beyond standard application by placing a specific focus on the use of AI techniques to address the challenges related to the different applications and topics of energy transition. The contributions are classified according to the market and actor interactions (service providers, manufacturers, customers, integrators, utilities etc.), to the SG architecture model (physical layer, infrastructure layer, and business layer), to the digital twin of SG (business model, operational model, fault/transient model, and asset model), and to the application domain (demand side management, load monitoring, micro grids, energy consulting (residents, utilities), energy saving, dynamic pricing revenue management and smart meters, etc.).
Download or read book Artificial Intelligence and Internet of Things for Renewable Energy Systems written by Neeraj Priyadarshi and published by Walter de Gruyter GmbH & Co KG. This book was released on 2021-11-22 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains the application of Artificial Intelligence and Internet of Things on green energy systems. The design of smart grids and intelligent networks enhances energy efficiency, while the collection of environmental data through sensors and their prediction through machine learning models improve the reliability of green energy systems.
Download or read book Business Development via AI and Digitalization written by Allam Hamdan and published by Springer Nature. This book was released on with total page 1112 pages. Available in PDF, EPUB and Kindle. Book excerpt:
