Introduces the basic procedures, standards, and instruments used to measure oil and gas. Intended as a primer for those who measure oil and gas and those who want to know how measurement procedures are performed. Can be used as an introduction for those new to the industry or as a reference for those knowledgeable about other areas of the industry but unfamiliar with measurement procedures and practices. Produced in cooperation with the API.

Eight lessons that correspond to the eight chapters in the book -- a Primer of Oil and Gas Measurement. Each lesson includes a summary of the chapter in the book, a vocabulary list, and a set of questions. These lessons can be used to reinforce the information presented in the book or as a pre- and/or post-test for each chapter. Answer sheets are included in the workbook, but can be deleted if the lessons are used as test material.

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)