The emergence of the international oil corporation as a political actor in the twentieth century, seen in BP's infrastructure and information arrangements in Iran. In the early twentieth century, international oil corporations emerged as a new kind of political actor. The development of the world oil industry, argues Katayoun Shafiee, was one of the era's largest political projects of techno-economic development. In this book, Shafiee maps the machinery of oil operations in the Anglo-Iranian oil industry between 1901 and 1954, tracking the organizational work involved in moving oil through a variety of technical, legal, scientific, and administrative networks. She shows that, in a series of disagreements, the British-controlled Anglo-Iranian Oil Company (AIOC, which later became BP) relied on various forms of information management to transform political disputes into techno-economic calculation, guaranteeing the company complete control over profits, labor, and production regimes. She argues that the building of alliances and connections that constituted Anglo-Iranian oil's infrastructure reconfigured local politics of oil regions and examines how these arrangements in turn shaped the emergence of both nation-state and transnational oil corporation. Drawing on her extensive archival and field research in Iran, Shafiee investigates the surprising ways in which nature, technology, and politics came together in battles over mineral rights; standardizing petroleum expertise; formulas for calculating profits, production rates, and labor; the “Persianization” of employees; nationalism and oil nationalization; and the long-distance machinery of an international corporation. Her account shows that the politics of oil cannot be understood in isolation from its technical dimensions. The open access edition of this book was made possible by generous funding from Knowledge Unlatched.

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.

Compression Machinery for Oil and Gas is the go-to source for all oil and gas compressors across the industry spectrum. Covering multiple topics from start to finish, this reference gives a complete guide to technology developments and their applications and implementation, including research trends. Including information on relevant standards and developments in subsea and downhole compression, this book aids engineers with a handy, single resource that will help them stay up-to-date on the compressors needed for today's oil and gas applications. - Provides an overview of the latest technology, along with a detailed discussion of engineering - Delivers on the efficiency, range and limit estimations for machines - Pulls together multiple contributors to balance content from both academics and corporate research

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)

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

The Oil Analysis Handbook is a volume in Coxmoor's Machine & System Condition Monitoring Series. Oil is considered as much an integral part of machine as any of its mechanical components and so must perform many vital functions over an extended period. By testing a sample of lubricant it is possible to measure its ability to continue its original function. This book sets out the concepts of oil analysis for the mature professional, the novice and student.