The book essentially covers the growing role of AI in the oil and gas industry, including digital technologies used in the exploration phase, customer sales service, and cloud-based digital storage of reservoir simulation data for modeling. It starts with the description of AI systems and their roles within the oil and gas industry, including the agent-based system, the impact of industrial IoT on business models, and the ethics of robotics in AI implementation. It discusses incorporating AI into operations, leading to the reduction of operating costs by localizing control functions, remote monitoring, and supervision. Features of this book are given as follows: It is an exclusive title on the application of AI and digital technology in the oil and gas industry It explains cloud data management in reservoir simulation It discusses intelligent oil and gas well completion in detail It covers marketing aspects of oil and gas business during the exploration phase It reviews development of digital systems for business purposes This book is aimed at professionals in petroleum and chemical engineering, technology, and engineering management.

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

"The book essentially covers the growing role of AI in oil and gas industry, including digital technology in exploration phase to customer sales service along with a cloud-based digital storage of reservoir simulation data for modeling. It initiates with the description of AI system and its role towards oil and gas business including agent base system, impact of industrial IoT on business models, ethics of robotics in AI implementation. It discusses reliability of operation leading to reduction of operating costs by localizing control functions, remote monitoring, and supervision. Features: Exclusive title on the application of AI and Digital Technology in the Oil and Gas industry. Explains Cloud Data Management in reservoir simulation. Discussed intelligent oil and gas-well completion in detail. Covers marketing aspect of oil and gas business during exploration phase. Reviews development of digital system for business purpose. This book aims at professionals in petroleum and chemical engineering, technology and engineering management"--

Applications of Artificial Intelligence Techniques in the Petroleum Industry gives engineers a critical resource to help them understand the machine learning that will solve specific engineering challenges. The reference begins with fundamentals, covering preprocessing of data, types of intelligent models, and training and optimization algorithms. The book moves on to methodically address artificial intelligence technology and applications by the upstream sector, covering exploration, drilling, reservoir and production engineering. Final sections cover current gaps and future challenges. - Teaches how to apply machine learning algorithms that work best in exploration, drilling, reservoir or production engineering - Helps readers increase their existing knowledge on intelligent data modeling, machine learning and artificial intelligence, with foundational chapters covering the preprocessing of data and training on algorithms - Provides tactics on how to cover complex projects such as shale gas, tight oils, and other types of unconventional reservoirs with more advanced model input

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)

The oil and gas industry is at a crossroads. Recent low prices, rapidly growing alternative fuels like renewables, the permanent swing from peak oil to super abundance, shifting consumer preferences, and global pressures to decarbonize suggest a challenged industry for the foreseeable future. Digital advances offer ways to lower costs of production, improve productivity, reduce carbon emissions, and regain public confidence. A wait-and-see attitude to digital innovation has failed many industries already, and the leaders of oil and gas urgently need guidance on how digital both disrupts and enhances their industry. Written by the world's leading experts on the intersection of digital technologies and the oil and gas industry, Bits, Bytes, and Barrels sets out the reasons why adoption is slow, describes the size and scale of both the opportunity and the threat from digital, identifies the key digital technologies and the role that they play in a digital future, and recommends a set of actions for leaders to take to accelerate the adoption of digital in the business. Providing an independent and expert perspective, Bits, Bytes, and Barrels addresses the impacts of digital across the breadth of the industry--from onshore to offshore, from upstream to midstream to integrated--and outlines a roadmap to help the decision-makers at all levels of the industry take meaningful action toward promising and rewarding digital adoption.

How is digitalization of the offshore oil industry fundamentally changing how we understand work and ways of knowing? Digitalization sits at the forefront of public and academic conversation today, calling into question how we work and how we know. In Digital Oil, Eric Monteiro uses the Norwegian offshore oil and gas industry as a lens to investigate the effects of digitalization on embodied labor, and in doing so shows how our use of new digital technology transforms work and knowing. For years, roughnecks have performed the dangerous and unwieldy work of extracting the oil that lies three miles below the seabed along the Norwegian Continental Shelf. Today, the Norwegian oil industry is largely digital, operated by sensors and driven by data. Digital representations of physical processes inform work practices and decision-making with remotely operated, unmanned deep-sea facilities. Drawing on two decades of in-depth interviews, observations, news clips, and studies of this industry, Eric Monteiro dismantles the divide between the virtual and the physical in Digital Oil. What is gained or lost when objects and processes become algorithmic phenomena with the digital inferred from the physical? How can data-driven work practices and operational decision-making approximate qualitative interpretation, professional judgement, and evaluation? How are emergent digital platforms and infrastructures, as machineries of knowing, enabling digitalization? In answering these questions Monteiro offers a novel analysis of digitalization as an effort to press the limits of quantification of the qualitative.