Machine Learning and Data Science in the Oil and Gas Industry
Machine Learning and Data Science in the Oil and Gas Industry

Author: Patrick Bangert

Publisher: Gulf Professional Publishing

Published: 2021-03-04

Total Pages: 290

ISBN-13: 0128209143

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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
Machine Learning Guide for Oil and Gas Using Python

Author: Hoss Belyadi

Publisher: Gulf Professional Publishing

Published: 2021-04-09

Total Pages: 478

ISBN-13: 0128219300

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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

Local Content Policies in the Oil and Gas Sector
Local Content Policies in the Oil and Gas Sector

Author: Silvana Tordo

Publisher: World Bank Publications

Published: 2013-07-22

Total Pages: 197

ISBN-13: 0821399349

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A number of countries have recently discovered and are developing oil and gas reserves. Policy makers in such countries are anxious to obtain the greatest benefits for their economies from the extraction of these exhaustible resources by designing appropriate policies to achieve desired goals. One important theme of such policies is the so-called local content created by the sector—the extent to which the output of the extractive industry sector generates further benefits to the economy beyond the direct contribution of its value-added, through its links to other sectors. While local content policies have the potential to stimulate broad-based economic development, their application in petroleum-rich countries has achieved mixed results. This paper describes the policies and practices meant to foster the development of economic linkages from the petroleum sector, as adopted by a number of petroleum-producing countries both in and outside the Organisation for Economic Co-operation and Development. Examples of policy objectives, implementation tools, and reporting metrics are provided to derive lessons of wider applicability. The paper presents various conclusions for policy makers about the design of local content policies.

Standard Handbook of Petroleum & Natural Gas Engineering
Standard Handbook of Petroleum & Natural Gas Engineering

Author: William C. Lyons

Publisher: Gulf Professional Publishing

Published: 1996

Total Pages: 1450

ISBN-13: 0884156427

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Volume 1 presents the mathematics and general engineering and science of petroleum engineering. It also examines the auxiliary equipment and provides coverage of all aspects of drilling and well completion.

Seismic Attributes as the Framework for Data Integration Throughout the Oilfield Life Cycle
Seismic Attributes as the Framework for Data Integration Throughout the Oilfield Life Cycle

Author: Kurt J. Marfurt

Publisher: SEG Books

Published: 2018-01-31

Total Pages: 509

ISBN-13: 1560803517

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Useful attributes capture and quantify key components of the seismic amplitude and texture for subsequent integration with well log, microseismic, and production data through either interactive visualization or machine learning. Although both approaches can accelerate and facilitate the interpretation process, they can by no means replace the interpreter. Interpreter “grayware” includes the incorporation and validation of depositional, diagenetic, and tectonic deformation models, the integration of rock physics systematics, and the recognition of unanticipated opportunities and hazards. This book is written to accompany and complement the 2018 SEG Distinguished Instructor Short Course that provides a rapid overview of how 3D seismic attributes provide a framework for data integration over the life of the oil and gas field. Key concepts are illustrated by example, showing modern workflows based on interactive interpretation and display as well as those aided by machine learning.