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The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry. Envisaging AI-inspired Intelligent Energy Systems and Environments. Edition No. 1. IEEE Press Series on Power and Energy Systems

  • Book

  • 512 Pages
  • November 2023
  • John Wiley and Sons Ltd
  • ID: 5866135
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.

Table of Contents

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

Authors

Pethuru Raj Chelliah Reliance Jio Platforms Ltd., Bangalore, India. Venkatraman Jayasankar Mats Agerstam Intel's Network and Edge Group, Portland, OR, USA. B. Sundaravadivazhagan University of Technology and Applied Sciences Al Mussanah, Oman. Robin Cyriac University of Technology and Applied Sciences Al Mussanah, Oman.