| Description |
1 online resource (513 p.) |
| Series |
IEEE Press Series on Power and Energy Systems Series |
|
IEEE Press Series on Power and Energy Systems Series
|
| Contents |
Cover -- Title Page -- Copyright Page -- Contents -- About the Authors -- Foreword -- Preface -- Chapter 1 A Perspective of the Oil and Gas Industry -- 1.1 Exploration and Production -- 1.1.1 Onshore -- 1.1.2 Offshore -- 1.1.3 Hydraulic Fracturing -- 1.2 Midstream Transportation -- 1.3 Downstream - Refining and Marketing -- 1.3.1 Hydrotreating -- 1.4 Meaning of Different Terms of Products Produced by the Oil and Gas Industry -- 1.4.1 Natural Gas -- 1.4.2 Extraction -- 1.4.3 Advantages and Disadvantages -- 1.4.4 Types -- 1.4.5 Types of Natural Gas Deposits |
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1.4.6 Conventional Natural Gas Deposits -- 1.4.7 Coal Bed Methane -- 1.4.8 Shale Gas -- 1.4.9 Tight Gas -- 1.4.10 Environmental Impacts of Natural Gas -- 1.4.11 The Future of Natural Gas -- 1.4.12 CNG vs. Liquid Fuels -- 1.4.13 Unconventional Oil and Gas -- 1.5 Oil and Gas Pricing -- 1.6 A Note on Renewable Energy Sources -- 1.6.1 Biomass -- 1.6.2 Hydropower -- 1.6.3 Geothermal -- 1.6.4 Wind -- 1.6.5 A Note About Hydrogen -- 1.6.6 How is Hydrogen Produced -- 1.6.7 Production of Hydrogen -- 1.6.8 Electrolysis -- 1.6.9 Biological Processes -- 1.6.10 Converting Hydrogen to Hydrogen-Based Fuels |
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1.7 Environmental Impact -- 1.8 Uses of Hydrogen -- 1.8.1 Challenges in Using Hydrogen as a Fuel -- Bibliography -- Chapter 2 Artificial Intelligence (AI) for the Future of the Oil and Gas (O&G) Industry -- 2.1 Introduction -- 2.2 The Emergence of Digitization Technologies and Tools -- 2.3 Demystifying Digitalization Technologies and Tools -- 2.4 Briefing the Potentials of Artificial Intelligence (AI) -- 2.5 AI for the Oil and Gas (O&G) Industry -- 2.5.1 Automate and Optimize Inspection -- 2.5.2 Defect Detection and Enhance Quality Assurance -- 2.5.3 Monitoring |
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2.5.4 Reduce Production and Maintenance Costs -- 2.5.5 Accurate Decision-Making -- 2.5.6 Improve Supply Chain and Logistics Efficiency -- 2.5.7 Geoscience Data Analytics -- 2.5.8 Predictive Models for Oil Field Development -- 2.5.9 Predictive Analytics for Reservoir Engineering -- 2.5.10 AI for Oil and Gas Production -- 2.5.11 AI in Midstream -- 2.5.12 AI in Downstream -- 2.6 Computer Vision (CV)-Enabled Use Cases -- 2.7 Natural Language Processing (NLP) Use Cases -- 2.8 Robots in the Oil and Gas Industry -- 2.9 Drones in the Oil and Gas Industry -- 2.9.1 Drones in Upstream Activities |
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2.9.2 Drones in Midstream Activities -- 2.9.3 Drones in Downstream Activities -- 2.10 AI Applications for the Oil and Gas (O&G) Industry -- 2.10.1 Optimizing Production and Scheduling -- 2.10.2 Asset Tracking and Maintenance Through AI-enabled Digital Twins (DT) -- 2.10.3 AI-led Cybersecurity -- 2.11 Better Decision-Making Using AI -- 2.11.1 Predictive Maintenance -- 2.11.2 Identifying Optimal Operating Condition -- 2.11.3 Well Logging -- 2.11.4 Detecting Contaminant Concentrations -- 2.11.5 Ensuring the People and Property Safety -- 2.11.6 Energy Efficiency -- 2.11.7 Equipment Inspection |
| Notes |
Description based upon print version of record |
|
2.12 Cloud AI vs. Edge AI for the Oil and Gas Industry |
| Genre/Form |
Electronic books
|
| Form |
Electronic book
|
| Author |
Jayasankar, Venkatraman
|
|
Agerstam, Mats
|
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Sundaravadivazhagan, B
|
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Cyriac, Robin
|
| ISBN |
9781119985594 |
|
1119985595 |
|
