The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry Comprehensive resource describing how operations, outputs, and offerings of the oil and gas industry can improve via advancements in AI
The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry describes the proven and promising digital technologies and tools available to empower the oil and gas industry to be future-ready. It shows how the widely reported limitations of the oil and gas industry are being nullified through the application of breakthrough digital technologies and how the convergence of digital technologies helps create new possibilities and opportunities to take this industry to its next level.
The text demonstrates how scores of proven digital technologies, especially in AI, are useful in elegantly fulfilling complicated requirements such as process optimization, automation and orchestration, real-time data analytics, productivity improvement, employee safety, predictive maintenance, yield prediction, and accurate asset management for the oil and gas industry.
The text differentiates and delivers sophisticated use cases for the various stakeholders, providing easy-to-understand information to accurately utilize proven technologies towards achieving real and sustainable industry transformation.
The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry includes information on:
How various machine and deep learning (ML/DL) algorithms, the prime modules of AI, empower AI systems to deliver on their promises and potential Key use cases of computer vision (CV) and natural language processing (NLP) as they relate to the oil and gas industry Smart leverage of AI, the Industrial Internet of Things (IIoT), cyber physical systems, and 5G communication Event-driven architecture (EDA), microservices architecture (MSA), blockchain for data and device security, and digital twins
Clearly expounding how the power of AI and other allied technologies can be meticulously leveraged by the oil and gas industry, The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry is an essential resource for students, scholars, IT professionals, and business leaders in many different intersecting fields.
About the Authors xxiii Foreword xxv Preface xxvii 1 A Perspective of the Oil and Gas Industry 1 1.1 Exploration and Production 2 1.2 Midstream Transportation 4 1.3 Downstream–Refining and Marketing 6 1.4 Meaning of Different Terms of Products Produced by the Oil and Gas Industry 7 1.5 Oil and Gas Pricing 16 1.6 A Note on Renewable Energy Sources 17 1.7 Environmental Impact 20 1.8 Uses of Hydrogen 20 2 Artificial Intelligence (AI) for the Future of the Oil and Gas (O&G) Industry 23 2.1 Introduction 23 2.2 The Emergence of Digitization Technologies and Tools 24 2.3 Demystifying Digitalization Technologies and Tools 25 2.4 Briefing the Potentials of Artificial Intelligence (AI) 25 2.5 AI for the Oil and Gas (O&G) Industry 27 2.6 Computer Vision (CV)-Enabled Use Cases 34 2.7 Natural Language Processing (NLP) Use Cases 36 2.8 Robots in the Oil and Gas Industry 36 2.9 Drones in the Oil and Gas Industry 37 2.10 AI Applications for the Oil and Gas (O&G) Industry 39 2.11 Better Decision-Making Using AI 41 2.12 Cloud AI vs. Edge AI for the Oil and Gas Industry 44 2.13 AI Model Optimization Techniques 47 2.14 Conclusion 48 3 Artificial Intelligence for Sophisticated Applications in the Oil and Gas Industry 51 3.1 Introduction 51 3.2 Oil and Gas Industry 52 3.3 Artificial Intelligence 54 3.4 Lifecycle of Oil and Gas Industry 54 3.5 Applications of AI in Oil and Gas industry 56 3.6 Chatbots 56 3.7 Optimized Procurement 59 3.8 Drilling, Production, and Reservoir Management 61 3.9 Inventory Management 62 3.10 Well Monitoring 64 3.11 Process Excellence and Automation 64 3.12 Asset Tracking and Maintenance/Digital Twins 65 3.13 Optimizing Production and Scheduling 67 3.14 Emission Tracking 68 3.15 Logistics Network Optimizations 69 3.16 Conclusion 70 4 Demystifying the Oil and Gas Exploration and Extraction Process 73 4.1 Process of Crude Oil Formation 73 4.2 Composition of Crude Oil 74 4.3 Crude Oil Classification 74 4.4 Crude Oil Production Process 76 4.5 Oil Exploration 77 4.6 Oil Extraction 78 4.7 Processing of Crude Oil 81 4.8 Overview of Refining 88 4.9 Marketing and Distribution of Oil and Gas 92 4.10 End of Production 93 4.11 Factors Influencing the Timing of Oil and Gas Exploration and Production 93 4.12 Non-revenue Benefits of the Oil and Gas Industry 95 4.13 Conclusion 95 5 Explaining the Midstream Activities in the Oil and Gas Domain 97 5.1 Introduction 97 5.2 Role of Midstream Sector in Oil and Gas Industry 98 5.3 Midstream Oil and Gas Operations 99 5.4 Technological Advancements in Midstream Sector 104 5.5 Midstream Sector Challenges 111 5.6 Conclusion 114 6 The Significance of the Industrial Internet of Things (IIoT) for the Oil and Gas Space 117 6.1 Overview of IIoT 117 6.2 Technical Innovators of Industrial Internet 125 6.3 IoT for Oil and Gas Sector 127 6.4 Rebellion of IoT in the Oil and Gas Sector 132 6.5 Oil and Gas Remote Monitoring Systems 136 6.6 Advantages of IIOT for the Oil and Gas Industry 142 6.7 Conclusion 144 7 The Power of Edge AI Technologies for Real-Time Use Cases in the Oil and Gas Domain 147 7.1 Introduction 147 7.2 Demystifying the Paradigm of Artificial Intelligence (AI) 148 7.3 Describing the Phenomenon of Edge Computing 149 7.4 Delineating Edge Computing Advantages 151 7.5 Demarcating the Move Toward Edge AI 154 7.6 Why Edge AI Gains Momentum? 155 7.7 The Enablers of Edge AI 160 7.8 5G-Advanced Communication 160 7.9 Why Edge AI is Being Pursued with Alacrity? 164 7.10 Edge AI Frameworks and Accelerators 165 7.11 Conclusion 175 8 AI-Enabled Robots for Automating Oil and Gas Operations 177 8.1 Briefing the Impending Digital Era 177 8.2 Depicting the Digital Power 178 8.3 Robotics: The Use Cases 181 8.4 Real-Life Examples of Robotic Solutions in the Oil and Gas Industry 184 8.5 The Advantages of Robotic Solutions 190 8.6 The Dawn of the Internet of Robotic Things 194 8.7 Conclusion 197 9 AI-Empowered Drones for Versatile Oil and Gas Use Cases 199 9.1 Introduction 199 9.2 The Upstream Process 200 9.3 The Midstream Process 201 9.4 The Downstream Process 202 9.5 Navigation Technologies for Drones 202 9.6 Drones Specialities and Successes 206 9.7 The Emergence of State-of-the-Art Drones 209 9.8 Drones in the Oil and Gas Industry 215 9.9 AI-Enabled Drone Services 217 9.10 AI Platforms for Drones 219 9.11 Conclusion 222 10 The Importance of Artificial Intelligence for the Oil and Gas Industry 224 10.1 Introduction 224 10.2 Reducing Well/Equipment Downtime 225 10.3 Optimizing Production and Scheduling 228 10.4 Detecting Anomalies by Enabling Automation in Assets using Robots 230 10.5 Inspection and Cleanliness of Reactors, Heat Exchangers, and Its Components 233 10.6 AI-Enabled Training and Safety 234 10.7 Summary 234 11 Illustrating the 5G Communication Capabilities for the Future of the Oil and Gas Industry 237 11.1 Introduction to 5G Communication 237 11.2 5G Architecture 243 11.3 Antennas For 5G 246 11.4 5G Use Cases 247 11.5 5G and Digitalization in Oil and Gas 252 11.6 5G Smart Monitoring Instruments 259 11.7 Conclusion 260 12 Delineating the Cloud and Edge-Native Technologies for Intelligent Oil and Gas Systems 263 12.1 Introduction 263 12.2 Cloud Native Technologies – Motivation 264 12.3 Containers 265 12.4 Microservices 268 12.5 Continuous Integration, Continuous Deployment (CI/CD) 274 12.6 Edge Computing 277 12.7 Conclusion 292 13 Explaining the Industrial IoT Standardization Efforts Toward Interoperability 293 13.1 Introduction 293 13.2 Different Aspects of Interoperability 293 13.3 ISA95 294 13.4 SCADA (Supervisory Control and Data Acquisition) 296 13.5 The Choice of Network Technology 296 13.6 OPAF 302 13.7 OPC-UA 305 13.8 DDS 310 13.9 Integration with Telemetry and Big Data 311 13.10 IEC Standards used in the OPAF 311 13.11 RedFish 312 13.12 The FieldComm Group 314 14 Digital Twins for the Digitally Transformed O&G Industry 316 14.1 Digital Twins (DTs) 316 14.2 Digital Twins in Manufacturing 316 14.3 Digital Twins in Process Efficiency 317 14.4 Digital Twins and Quality Assurance 317 14.5 Digital Twins and Supply Chain 317 14.6 Digital Twins and Predictive Maintenance 317 14.7 Industry 4.0 318 14.8 Digital Twin Concept 319 14.9 Standards and Interoperability 320 14.10 IDTA Standard 321 14.11 Digital Twin Consortium 322 14.12 Digital Twin in O&G 322 14.13 DT Complexity and Trade-offs 323 14.14 Architectural Concepts 323 14.15 Simulations 324 14.16 Digital Twins vs. Simulations 327 14.17 Digital Twin Products 328 14.18 Digital Twins and Manufacturing in the Future 329 15 IoT Edge Security Methods for Secure and Safe Oil and Gas Environments 331 15.1 Introduction 331 15.2 Protecting Data 332 15.3 Past Examples of Security Attacks 332 15.4 Security Foundation 334 15.5 Cryptographic Hash Function 337 15.6 Keyed Hash Message Authentication Code 338 15.7 Public Key Infrastructure (PKI) 338 15.8 Digital Signatures 340 15.9 Threat Analysis and Understanding Adversaries 341 15.10 Trusted Computing Base 342 15.11 Edge Security and RoT (Root of Trust) 342 15.12 DICE – Device Identifier Composition Engine 343 15.13 Boot Integrity 343 15.14 Data Sanitization 345 15.15 Total Memory Encryption 346 15.16 Secure Device Onboarding 347 15.17 Attestation 350 15.18 Defense in Depth 352 15.19 Zero Trust Architecture (ZTA) 354 15.20 Security Hardened Edge Compute Architectures 354 16 Securing the Energy Industry with AI-Powered Cybersecurity Solutions 356 16.1 Introduction 356 16.2 Energy Industry 357 16.3 Present and Future of Energy Industry Supply Chain 359 16.4 Cybersecurity 361 16.5 Digitizing of the Energy Industry 364 16.6 MITRE ATT&CK Framework 367 16.7 CVE 368 16.8 CWE 370 16.9 CAPEC 370 16.10 CPE 370 16.11 Cybersecurity Framework 370 16.12 NIST Framework 371 16.13 Zero-Day Vulnerability 372 16.14 Machine Learning 373 16.15 Artificial Intelligence 374 16.16 Fusing AI into Cybersecurity 375 16.17 Threat Modeling in AI 379 16.18 Incident Response 382 16.19 Fire Sale Scenario 383 16.20 Conclusion 384 17 Explainable Artificial Intelligence (XAI) for the Trust and Transparency of the Oil and Gas Systems 387 17.1 Introduction 387 17.2 The Growing Power of Artificial Intelligence 388 17.3 The Challenges and Concerns of Artificial Intelligence 390 17.4 About the Need for AI Explainability 391 17.5 AI Explainability: The Problem It Solves 392 17.6 What is the AI Explainability Challenges? 393 17.7 The Importance of Explainable AI 393 17.8 The Importance of Model Interpretation 396 17.9 Briefing Feature Importance Scoring Methods 401 17.10 Local Interpretable Model-agnostic Explanations (LIME) 402 17.11 SHAP Explainability Algorithm 404 17.12 Conclusion 407 18 Blockchain for Enhanced Efficiency, Trust, and Transparency in the Oil and Gas Domain 409 18.1 Introduction 409 18.2 The Brewing Challenges of the Oil and Gas Industry 410 18.3 About the Blockchain Technology 413 18.4 Blockchain-Powered Use Cases for the Oil and Gas Industry 415 18.5 Blockchain for Improved Trust 416 18.6 Sensor-Enabled Invoicing 417 18.7 Transportation Tracing 418 18.8 Data Storage and Management 419 18.9 Digital Oil and Gas: Strengthening and Simplifying Supply Chain 419 18.10 Commodity Trading 421 18.11 Land Record Management 421 18.12 Financial Reconciliation 422 18.13 Oil Wells and Equipment Maintenance 423 18.14 Waste Management and Recycling 423 18.15 Tracking Carbon Footprint 424 18.16 Improved Pipeline Inspection 424 18.17 Other Miscellaneous Advantages of Blockchain 425 18.18 Blockchain Challenges 425 18.19 Conclusion 426 19 AI-Inspired Digital Twins for the Oil and Gas Domain 428 19.1 How to Ensure Certainty Using DT for AI 432 19.2 Tools Needed to Develop Digital Twins 434 19.3 Digital Twin Implementation Approach at a High Level 434 19.4 Digital Twin of Oil and Gas Production 441 19.5 Solution Approach 442 19.6 Future of Digital Twins 443 20 Future Directions of Green Hydrogen and Other Fueling Sources 447 20.1 Introduction 447 20.2 Green Hydrogen Technologies 448 20.3 Current and Future Industrial Applications of Hydrogen 449 20.4 The Exploitation of Hydrogen Fuel in a Future System 450 20.5 Green Hydrogen: Fuel of the Future 451 20.6 Extraction of Hydrogen with Diagrammatic Representation 453 20.7 Hydrogen Fuel System Advantages and Disadvantages 454 20.8 AI-Based Approach for Emerging Green Hydrogen Technologies for Sustainability 455 20.9 Challenges of Hydrogen with AI Technologies 458 20.10 The Expected Use and Forecast for Hydrogen Fuel Cells in the Future 458 20.11 Conclusion 459 Bibliography 460 Index 461
Pethuru Raj Chelliah, PhD, is the Vice President and Chief Architect at Reliance Jio Platforms Ltd. In Bangalore, India. Venkatraman Jayasankar is the Lead Solutions Architect (Upstream) at a leading oil and gas company. Mats Agerstam is a Principal Engineer at Intel’s Network and Edge Group, Portland, Oregon, USA. B. Sundaravadivazhagan, PhD, is a Professor with the Department of Information Technology at the University of Technology and Applied Sciences Al Mussanah, Oman. Robin Cyriac, PhD, is a Professor with the Department of Information Technology at the University of Technology and Applied Sciences Al Mussanah, Oman.
