ASME STB 1 2020

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ASME STB-1 – 2020: Guideline on Big Data/Digital Transformation Workflows and Applications for the Oil and Gas Industry

Published By	Publication Date	Number of Pages
ASME	2020	104

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Description

The guideline explains the current use and application of data analytics and data science in the oil and gas industry. It is designed to provide guidance on how to utilize data analytics and machine learning/artificial intelligence (ML/AI) to address a given business need, resulting in value-creation. This guideline provides descriptions of various data analytics techniques and the recommended tools for the respective techniques and a framework for understanding and a workflow for utilizing data analytical techniques to solve business problems, without requiring the reader to be a full-time statistician or data scientist professional. It is universal in its application to Big Data challenges in the oil and gas industry and is written not only for oil and gas professionals who are beginners to Big Data techniques, but also for data professionals looking to contribute to unique oil and gas applications. Specific users could include: Citizen Data Scientist: a subject matter expert in engineering, operations, supply chain, planning, project management or operations that requires data insights. Early Career Engineer: a young professional that is looking to improve his or her career by adding a data dimension to their problem solving. Data Scientist Professional: a data science professional that is looking to apply his or her deep experience in data analytics by learning the unique sets of data and operational challenges of the oil and gas industry. This document is the culmination of the efforts of ASME industry professionals in the oil and gas industry to define Big Data and its useful applications to upstream, midstream and downstream businesses.

PDF Catalog

PDF Pages	PDF Title
8	Foreword
10	1 Purpose, Definitions and References 1.1 Scope 1.1.1 How to Use This Guideline
11	1.2 Definitions
19	1.3 References
20	2 Data Structure and Management 2.1 Introduction 2.2 Structured Data 2.2.1 Types and Usage 2.2.2 Databases 2.2.3 Examples
22	2.3 Unstructured Data 2.3.1 Types and Usage 2.3.2 Respective Databases 2.3.3 Examples
24	2.4 Security and Governance of Data 2.4.1 Responsibility of the Enterprise 2.4.2 Key Concepts of Information Security 2.4.3 Data Protection
25	2.4.4 Developing Software that is Secure 2.4.5 Facility Management Systems
26	3 Big Data in the Oil and Gas Industry 3.1 Introduction 3.1.1 Overview 3.1.2 Oil and Gas Facility Lifecycle Digital Requirements
28	3.1.3 Designing the Digital Facility 3.1.4 Understanding the Data in Oil and Gas Activities 3.2 Hydrocarbon Reservoirs, Drilling, Production, Transportation and Refining 3.2.1 Activities that Produce Data
34	3.2.2 Digital Facility Descriptions
35	3.3 Mechanical Equipment and Instrumentation 3.3.1 Pressure Control Equipment 3.3.2 Rotating Equipment 3.3.3 Electrical and Instrumentation
36	3.3.4 Process Control 3.3.5 Process Equipment
37	3.3.6 Civil/Structural 3.4 Pipelines/Storage 3.5 Operations 3.6 MetOcean
38	3.7 Health and Safety 3.8 Supply Chain
39	3.9 Special Note to this Chapter
40	4 Methods of Analysis 4.1 General Information on How and When to Use These Methods
42	4.2 Descriptive Analytics and Data Mining 4.2.1 Importance and Objectives
43	4.2.2 General Statistical Descriptors 4.2.3 Descriptive Analytical Tools
44	4.3 Predictive Analytics 4.3.1 Importance and Objectives
45	4.3.2 Regression Problems and Solutions 4.3.3 Classification Problems and Solutions
47	4.3.4 Unstructured Data Problems and Solutions
48	4.3.5 Time Series 4.4 Prescriptive Analytics 4.4.1 Importance and Objectives 4.4.2 Optimization Problems and Solutions 4.4.3 Simulation Problems and Solutions 4.5 Application Program Interfaces 4.5.1 Importance and Objectives
49	4.5.2 Implementation 4.6 Visualization Tools
50	5 Data Analytics Project Workflows 5.1 Introduction 5.1.1 CRISP-DM
51	5.1.2 INFORMS and the Job Task Analysis 5.1.3 Structure, Roles and Responsibilities 5.1.4 Value to the Enterprise
52	5.2 Business Problem Framing 5.2.1 Description
53	5.2.2 Team Member Roles 5.2.3 Example Business Challenge – Permian Basin Production Forecasting
54	5.3 Analytics Problem Framing 5.3.1 Description 5.3.2 Team Member Roles
55	5.3.3 Example Business Challenge – Permian Basin Forecasting Model Continued 5.4 Data 5.4.1 Description
56	5.4.2 Team Member Roles
57	5.4.3 Example Business Challenge – Permian Basin Forecasting Model Continued
58	5.5 Methodology Approach and Selection 5.5.1 Description
59	5.5.2 Team Member Roles 5.5.3 Example Business Challenge – Permian Basin Forecasting Model Continued 5.6 Model Building and Testing 5.6.1 Description
60	5.6.2 Team Member Roles 5.6.3 Example Business Challenge – Permian Basin Forecasting Model Continued
61	5.7 Solution Deployment 5.7.1 Description
62	5.7.2 Team Member Roles 5.7.3 Permian Basin Forecasting Model Continued 5.8 Model Maintenance and Recycle 5.8.1 Description
63	5.8.2 Team Member Roles 5.8.3 Example Business Challenge – Permian Basin Forecasting Model Concluded
64	5.9 The Business Solution 5.9.1 The Continuing Challenge 5.9.2 The Important Role of the Engineer
65	Mandatory Appendix I: Data Characterization Chart for Oil and Gas
66	I-1 Digital Twin Representation Example
68	Mandatory Appendix II
69	Appendix I: Appendix J: Appendix K: Appendix L: Appendix M: Appendix N: Appendix O: Appendix P: Appendix Q: Appendix R: Appendix S: Appendix T: Appendix U: Appendix V: Appendix W: Appendix X: Appendix Y: Appendix Z: Appendix AA: Appendix BB: Appendix CC: Appendix DD: Appendix EE: Appendix FF: Appendix GG: Appendix HH: II-1 Detailed Data Journey
70	II-2 SIPOC Chart
71	II-3 Job Function Descriptions
72	II-4 RACI Chart
73	Nonmandatory Appendix A: Case Study
87	Nonmandatory Appendix B: Certifications Available
89	Nonmandatory Appendix C: Glossary Definitions
103	Copyright Declarations

Additional information

Standard Title	ASME STB-1 – 2020: Guideline on Big Data/Digital Transformation Workflows and Applications for the Oil and Gas Industry
Published Code	ASME
Publication Date	2020
Pages Count	104

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