The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry

Name: The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry | Envisaging AI-inspired Intelligent Energy Systems and Environments
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Envisaging AI-inspired Intelligent Energy Systems and Environments

Pethuru Raj Chelliah Venkatraman Jayasankar Mats Agerstam B. Sundaravadivazhagan Robin Cyriac(Autor*in)

Wiley (Verlag)

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Erschienen am 28. November 2023

512 Seiten

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Beschreibung

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.

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Inhalt

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
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
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
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
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
2.12 Cloud AI vs. Edge AI for the Oil and Gas Industry
2.13 AI Model Optimization Techniques
2.14 Conclusion
Bibliography
Chapter 3 Artificial Intelligence for Sophisticated Applications in the Oil and Gas Industry
3.1 Introduction
3.2 Oil and Gas Industry
3.2.1 Upstream: Production and Exploration
3.2.2 Midstream: Transportation
3.2.3 Downstream: Refining and Marketing
3.3 Artificial Intelligence
3.4 Lifecycle of Oil and Gas Industry
3.4.1 Exploration
3.4.2 Appraisal
3.4.3 Development
3.4.4 Production
3.4.5 Decommissioning
3.5 Applications of AI in Oil and Gas industry
3.6 Chatbots
3.7 Optimized Procurement
3.8 Drilling, Production, and Reservoir Management
3.9 Inventory Management
3.10 Well Monitoring
3.11 Process Excellence and Automation
3.12 Asset Tracking and Maintenance/Digital Twins
3.13 Optimizing Production and Scheduling
3.14 Emission Tracking
3.15 Logistics Network Optimizations
3.16 Conclusion
References
Chapter 4 Demystifying the Oil and Gas Exploration and Extraction Process
4.1 Process of Crude Oil Formation
4.2 Composition of Crude Oil
4.3 Crude Oil Classification
4.3.1 Other Types of Crude Oil
4.4 Crude Oil Production Process
4.5 Oil Exploration
4.6 Oil Extraction
4.6.1 Bringing Extracted Crude Oil to the Surface
4.6.2 Enhanced Oil Recovery
4.7 Processing of Crude Oil
4.7.1 Oil and Natural Gas Storage
4.7.2 Oil and Gas Transportation
4.7.3 Tanker Ships
4.7.4 Railcars
4.7.5 Tank Trucks
4.8 Overview of Refining
4.8.1 Separation/Distillation
4.8.2 Conversion
4.8.3 Enhancement
4.8.4 Blending/Finishing
4.8.5 Types of Refineries
4.9 Marketing and Distribution of Oil and Gas
4.10 End of Production
4.11 Factors Influencing the Timing of Oil and Gas Exploration and Production
4.11.1 Physical and Technical Factors
4.11.2 Social and Political Factors
4.11.3 Business Coordination Factors
4.12 Non-revenue Benefits of the Oil and Gas Industry
4.13 Conclusion
References
Chapter 5 Explaining the Midstream Activities in the Oil and Gas Domain
5.1 Introduction
5.2 Role of Midstream Sector in Oil and Gas Industry
5.3 Midstream Oil and Gas Operations
5.3.1 Field Processing
5.3.2 Storage
5.3.3 Transportation
5.4 Technological Advancements in Midstream Sector
5.4.1 Cloud Computing
5.4.2 Internet of Things
5.4.3 Robotics and Automation
5.4.4 3D Technology
5.4.5 Manufacturing and Execution Systems
5.5 Midstream Sector Challenges
5.5.1 Cyber-Attacks
5.5.2 Environmental Considerations
5.5.3 Social Concerns
5.5.4 Regulations
5.6 Conclusion
References
Chapter 6 The Significance of the Industrial Internet of Things (IIoT) for the Oil and Gas Space
6.1 Overview of IIoT
6.1.1 Functioning of Internet of Things
6.1.2 IIOT Viewpoints
6.1.3 Benefits of IIoT
6.1.4 Security in IIoT
6.2 Technical Innovators of Industrial Internet
6.2.1 Industrial Control Systems (ICS)
6.2.2 Supervisory Control and Data Acquisition (SCADA)
6.3 IoT for Oil and Gas Sector
6.3.1 Utilizing IIoT in Oil and Gas
6.3.2 Excellence in Operations
6.3.3 Device Management
6.3.4 Device Connectivity
6.3.5 Transformation and Storage
6.3.6 Presentation and Action
6.3.7 Microsoft Azure
6.4 Rebellion of IoT in the Oil and Gas Sector
6.4.1 Improved Operational Efficiency
6.4.2 Optimize Inventory Levels Based on Actual Usage
6.4.3 Improve Stockroom Management
6.4.4 Return on Investment (ROI)/Revenue
6.4.5 Real-Time Monitoring
6.4.6 Removing Manual Measuring Processes
6.4.7 Reduction of Safety Risk
6.4.8 Hurdles in the Oil and Gas Sector
6.5 Oil and Gas Remote Monitoring Systems
6.5.1 Sensors
6.5.2 Smart Algorithms
6.5.3 Prognostic and Preemptive Maintenance
6.5.4 Robots and Drones
6.5.5 Smart Accessories
6.5.6 Wearable Watches
6.5.7 Wearable Glasses
6.5.8 Drones
6.5.9 Monitoring Critical Systems 24/7
6.5.10 PLC Emergency Alert Notification Systems
6.5.11 Independent Verification
6.5.12 Oil and Gas Survey and Manufacturing Process
6.6 Advantages of IIOT for the Oil and Gas Industry
6.6.1 Monitoring Pipelines
6.6.2 Risk Mitigation
6.6.3 Environmental Impact
6.6.4 Managing Emergency Conditions
6.6.5 Establishing Workers Healthy and Safety
6.6.6 Supply Chain Management
6.7 Conclusion
Bibliography
Chapter 7 The Power of Edge AI Technologies for Real-Time Use Cases in the Oil and Gas Domain
7.1 Introduction
7.2 Demystifying the Paradigm of Artificial Intelligence (AI)
7.3 Describing the Phenomenon of Edge Computing
7.4 Delineating Edge Computing Advantages
7.4.1 The Formation of Edge Device Clouds
7.4.2 Real-Time Computing
7.4.3 Real-Time Analytics
7.4.4 Scalable Computing
7.4.5 Secured Computing
7.4.6 Automated Analytics and Action
7.4.7 Reduced Costs
7.5 Demarcating the Move Toward Edge AI
7.5.1 How Edge AI Helps to Generate Better Business
7.6 Why Edge AI Gains Momentum?
7.6.1 The Growing Device Ecosystem
7.6.2 Federated Learning
7.6.3 Optimization Techniques to Run AI Models in Edge Devices
7.6.4 Neural Network (NN) Pruning
7.6.5 L2 and L1 Regularization
7.7 The Enablers of Edge AI
7.8 5G-Advanced Communication
7.8.1 Industrial 5G
7.8.2 Edge Computing
7.8.3 Massive Amounts of Edge Devices Data
7.8.4 The Emergence of Accelerators and Specialized Engines
7.8.5 Cyber Physical Systems (CPS)
7.8.6 Digital Twins (DT)
7.9 Why Edge AI is Being Pursued with Alacrity?
7.9.1 The Need for Customer Delight
7.9.2 Unearthing Fresh Use Cases for Edge AI Across Industrial Verticals
7.10 Edge AI Frameworks and Accelerators
7.11 Conclusion
Bibliography
Chapter 8 AI-Enabled Robots for Automating Oil and Gas Operations
8.1 Briefing the Impending Digital Era
8.2 Depicting the Digital Power
8.2.1 The Emergence of Advanced Drones
8.2.2 The Grandiose Arrival of the State-of-the-Art Robots
8.3 Robotics: The Use Cases
8.3.1 Upstream Oil and Gas
8.3.2 Midstream Oil and Gas
8.3.3 Downstream Oil and Gas
8.4 Real-Life Examples of Robotic Solutions in the Oil and Gas Industry
8.5 The Advantages of Robotic Solutions
8.6 The Dawn of the Internet of Robotic Things
8.6.1 The Hardware Layer
8.6.2 The Network Layer
8.6.3 The Internet Layer
8.6.4 Infrastructure Layer
8.6.5 The Application Layer
8.7 Conclusion
Bibliography
Chapter 9 AI-Empowered Drones for Versatile Oil and Gas Use Cases
9.1 Introduction
9.2 The Upstream Process
9.3 The Midstream Process
9.4 The Downstream Process
9.5 Navigation Technologies for Drones
9.5.1 5G Enables Seamless Communication for Drones
9.5.2 5G Drones for the Oil and Gas (O&G) Industry
9.6 Drones Specialities and Successes
9.6.1 Drones Assist Human Workers
9.6.2 Drones Enhance the Safety and Security
9.6.3 Drones to Analyze Surroundings and Avoid Obstacles
9.6.4 Positioning Capability to Achieve Failsafe Function
9.6.5 Increased Safety
9.6.6 Less Downtime
9.6.7 Cost Savings
9.7 The Emergence of State-of-the-Art Drones
9.7.1 Drones with Real-Time Flight Parameters
9.7.2 Connected Drones
9.7.3 Cognitive Drones
9.7.4 Integration with Artificial Intelligence (AI)
9.8 Drones in the Oil and Gas Industry
9.9 AI-Enabled Drone Services
9.9.1 Drones with AI Power
9.9.2 Sensors for Data Collection
9.10 AI Platforms for Drones
9.11 Conclusion
Bibliography
Chapter 10 The Importance of Artificial Intelligence for the Oil and Gas Industry
10.1 Introduction
10.2 Reducing Well/Equipment Downtime
10.3 Optimizing Production and Scheduling
10.4 Detecting Anomalies by Enabling Automation in Assets using Robots
10.5 Inspection and Cleanliness of Reactors, Heat Exchangers, and Its Components
10.6 AI-Enabled Training and Safety
10.7 Summary
Bibliography
Chapter 11 Illustrating the 5G Communication Capabilities for the Future of the Oil and Gas Industry
11.1 Introduction to 5G Communication
11.1.1 Essentials of 5G Networks
11.1.2 Characteristic of 5G Networks
11.1.3 Applications of 5G
11.2 5G Architecture
11.2.1 5G E2E Network Architecture
11.2.2 Network Slicing Architecture
11.2.3 5G Mobile Network Architecture Design
11.3 Antennas For 5G
11.3.1 Circular Patch Antenna with Three Notches
11.3.2 MIMO Dual-Band Eight-Antenna Cluster Layout
11.3.3 Leaky-Wave Antenna
11.3.4 Magnetoelectric Dipole Antenna with Circular Polarization
11.4 5G Use Cases
11.4.1 Enhanced Mobile Broadband (EMBB)
11.4.2 Massive Machine-Type Connectivity (MMTC)
11.4.3 Ultra-Reliable Low-Latency Communication (URLLC)
11.4.4 Increasing Agricultural Productivity
11.4.5 Optimized Remote Knowledge Acquisition
11.4.6 Improved Supply Chain
11.4.7 Advanced Medical Services
11.4.8 Advanced Production Methods
11.4.9 Revamped Mining, Oil, and Gas Operations
11.4.10 Customized and Productive Retail
11.4.11 Smarter Administration Presidency and Systems
11.4.12 Efficient and Productive Infrastructure
11.4.13 Massive Support for the Labour Force
11.5 5G and Digitalization in Oil and Gas
11.5.1 Intelligent Automation and Economic Contributions of 5G Networks
11.5.2 5G for the Internet of Things (IOT)
11.5.3 IoT and 5G in Retail Applications
11.5.4 IOT and 5G in Manufacturing Industries
11.5.5 5G in the Oil and Gas Sector
11.6 5G Smart Monitoring Instruments
11.6.1 Multi-Vision Smart Surveillance (MVSS)
11.6.2 Wearable Webcam
11.6.3 3D Augmented/Virtual Reality (AR/VR) for Diagnostics and Preservation
11.6.4 Unmanned Aerial Vehicle Investigation and Video Monitoring
11.7 Conclusion
References
Chapter 12 Delineating the Cloud and Edge-Native Technologies for Intelligent Oil and Gas Systems
12.1 Introduction
12.2 Cloud Native Technologies - Motivation
12.3 Containers
12.3.1 Containers and the Operating System
12.3.2 Namespaces
12.3.3 cgroups or Control Groups
12.3.4 Container Interoperability
12.3.5 Container Images
12.3.6 Container Registries
12.4 Microservices
12.4.1 From Monolith to Microservices
12.4.2 When Did Microservices Start Emerging?
12.4.3 Microservices as a Design Pattern
12.4.4 Characteristics of Microservices
12.4.5 Advantages
12.4.6 Disadvantages
12.4.7 The Service Interface HTTP and gRPC
12.4.8 Persistent Storage and Databases
12.5 Continuous Integration, Continuous Deployment (CI/CD)
12.5.1 Interface Specification
12.5.2 DevOps
12.6 Edge Computing
12.6.1 Motivation
12.6.2 Edge Architecture
12.6.3 Orchestration vs. Container Management
12.6.4 Device Manageability
12.6.5 Device Life Cycle
12.6.6 Kubernetes
12.6.7 Declarative Paradigm
12.6.8 Packaging and Helm
12.6.9 Deployment of a Service
12.6.10 Multicluster Orchestration
12.6.11 Serverless
12.6.12 Serverless at the Edge
12.6.13 Apache OpenWhisk
12.6.14 Knative
12.6.15 IoT Devices
12.6.16 Device Twin
12.7 Conclusion
References
Chapter 13 Explaining the Industrial IoT Standardization Efforts Toward Interoperability
13.1 Introduction
13.2 Different Aspects of Interoperability
13.3 ISA95
13.4 SCADA (Supervisory Control and Data Acquisition)
13.5 The Choice of Network Technology
13.5.1 Wi-Fi
13.5.2 LoRAWAN
13.5.3 Time Slotted Technologies
13.5.4 Cellular Technologies
13.6 OPAF
13.7 OPC-UA
13.7.1 Objectives of OPC-UA
13.7.2 OPC-UA Architecture Overview
13.7.3 Publish/Subscribe Pattern
13.7.4 OPC-UA Pub/Sub
13.7.5 MQTT vs. AMQP
13.7.6 Future Pub/Sub Integrations
13.8 DDS
13.9 Integration with Telemetry and Big Data
13.10 IEC Standards used in the OPAF
13.11 RedFish
13.12 The FieldComm Group
References
Chapter 14 Digital Twins for the Digitally Transformed O&G Industry
14.1 Digital Twins (DTs)
14.2 Digital Twins in Manufacturing
14.3 Digital Twins in Process Efficiency
14.4 Digital Twins and Quality Assurance
14.5 Digital Twins and Supply Chain
14.6 Digital Twins and Predictive Maintenance
14.7 Industry 4.0
14.8 Digital Twin Concept
14.9 Standards and Interoperability
14.10 IDTA Standard
14.11 Digital Twin Consortium
14.12 Digital Twin in O&G
14.13 DT Complexity and Trade-offs
14.14 Architectural Concepts
14.15 Simulations
14.16 Digital Twins vs. Simulations
14.17 Digital Twin Products
14.18 Digital Twins and Manufacturing in the Future
References
Chapter 15 IoT Edge Security Methods for Secure and Safe Oil and Gas Environments
15.1 Introduction
15.2 Protecting Data
15.3 Past Examples of Security Attacks
15.4 Security Foundation
15.5 Cryptographic Hash Function
15.6 Keyed Hash Message Authentication Code
15.7 Public Key Infrastructure (PKI)
15.8 Digital Signatures
15.9 Threat Analysis and Understanding Adversaries
15.10 Trusted Computing Base
15.11 Edge Security and RoT (Root of Trust)
15.12 DICE - Device Identifier Composition Engine
15.13 Boot Integrity
15.14 Data Sanitization
15.15 Total Memory Encryption
15.16 Secure Device Onboarding
15.17 Attestation
15.18 Defense in Depth
15.19 Zero Trust Architecture (ZTA)
15.20 Security Hardened Edge Compute Architectures
References
Chapter 16 Securing the Energy Industry with AI-Powered Cybersecurity Solutions
16.1 Introduction
16.2 Energy Industry
16.2.1 Non-renewable Energy
16.2.2 Renewable Energy
16.3 Present and Future of Energy Industry Supply Chain
16.4 Cybersecurity
16.5 Digitizing of the Energy Industry
16.6 MITRE ATT&CK Framework
16.7 CVE
16.8 CWE
16.9 CAPEC
16.10 CPE
16.11 Cybersecurity Framework
16.12 NIST Framework
16.13 Zero-Day Vulnerability
16.14 Machine Learning
16.15 Artificial Intelligence
16.16 Fusing AI into Cybersecurity
16.16.1 Threat Hunting Enhancement
16.16.2 Detect Intrusions and Malwares
16.16.3 Discover Code Vulnerabilities
16.16.4 Enhance Network Security
16.16.5 Detect Phishing
16.16.6 Predict Breach Risks
16.17 Threat Modeling in AI
16.18 Incident Response
16.19 Fire Sale Scenario
16.20 Conclusion
References
Chapter 17 Explainable Artificial Intelligence (XAI) for the Trust and Transparency of the Oil and Gas Systems
17.1 Introduction
17.2 The Growing Power of Artificial Intelligence
17.2.1 The Growing Scope of AI for the Oil and Gas Industry
17.2.2 Explainable AI for the Oil and Gas Industry
17.3 The Challenges and Concerns of Artificial Intelligence
17.4 About the Need for AI Explainability
17.4.1 What Is AI Explainability?
17.5 AI Explainability: The Problem It Solves
17.6 What is the AI Explainability Challenges?
17.6.1 Mitigate AI Risk with Explainability
17.7 The Importance of Explainable AI
17.8 The Importance of Model Interpretation
17.8.1 Model Transparency
17.8.2 Start with Interpretable Algorithms
17.8.3 Standard Techniques for Model Interpretation
17.8.4 ROC Curve
17.8.5 Focus on Feature Importance
17.8.6 Partial Dependence Plots (PDPs)
17.8.7 Global Surrogate Models
17.9 Briefing Feature Importance Scoring Methods
17.10 Local Interpretable Model-agnostic Explanations (LIME)
17.11 SHAP Explainability Algorithm
17.11.1 Advance the Trust with Symbolic AI
17.12 Conclusion
Bibliography
Chapter 18 Blockchain for Enhanced Efficiency, Trust, and Transparency in the Oil and Gas Domain
18.1 Introduction
18.2 The Brewing Challenges of the Oil and Gas Industry
18.2.1 Harmful Unconventional Resources
18.2.2 Rising Consumer Demand
18.2.3 Social and Environmental Responsibility
18.2.4 Increased Demands for Renewable Fuels
18.2.5 Increasing Costs for Maintenance
18.2.6 Decreasing Performance
18.2.7 Inefficient Supply Chain
18.2.8 Lack of Transparency
18.2.9 Lack of Proper Audits and Maintenance
18.2.10 Poor Waste Management
18.3 About the Blockchain Technology
18.3.1 The Power of Blockchain
18.4 Blockchain-Powered Use Cases for the Oil and Gas Industry
18.4.1 Digitization and Digitalization for Operational Efficiency
18.5 Blockchain for Improved Trust
18.5.1 Transparency in Energy Markets
18.6 Sensor-Enabled Invoicing
18.6.1 Hydrocarbon Tracking and Easy Record Keeping
18.7 Transportation Tracing
18.7.1 Crypto-backed Oil and Gas
18.7.2 Compliance Automation
18.7.3 Environmental Compliance
18.8 Data Storage and Management
18.9 Digital Oil and Gas: Strengthening and Simplifying Supply Chain
18.10 Commodity Trading
18.11 Land Record Management
18.12 Financial Reconciliation
18.13 Oil Wells and Equipment Maintenance
18.14 Waste Management and Recycling
18.14.1 Management of Wastewater
18.15 Tracking Carbon Footprint
18.16 Improved Pipeline Inspection
18.16.1 Error Reduction
18.17 Other Miscellaneous Advantages of Blockchain
18.18 Blockchain Challenges
18.19 Conclusion
Bibliography
Chapter 19 AI-Inspired Digital Twins for the Oil and Gas Domain
19.1 How to Ensure Certainty Using DT for AI
19.1.1 Capture
19.1.2 Interpret
19.1.3 Consolidate
19.1.4 The Types of Data that a DT gets
19.2 Tools Needed to Develop Digital Twins
19.2.1 Engineering Modeling/Drawing Tools
19.2.2 IoT Devices and Tools
19.3 Digital Twin Implementation Approach at a High Level
19.4 Digital Twin of Oil and Gas Production
19.5 Solution Approach
19.6 Future of Digital Twins
Bibliography
Chapter 20 Future Directions of Green Hydrogen and Other Fueling Sources
20.1 Introduction
20.2 Green Hydrogen Technologies
20.3 Current and Future Industrial Applications of Hydrogen
20.4 The Exploitation of Hydrogen Fuel in a Future System
20.5 Green Hydrogen: Fuel of the Future
20.6 Extraction of Hydrogen with Diagrammatic Representation
20.7 Hydrogen Fuel System Advantages and Disadvantages
20.8 AI-Based Approach for Emerging Green Hydrogen Technologies for Sustainability
20.9 Challenges of Hydrogen with AI Technologies
20.10 The Expected Use and Forecast for Hydrogen Fuel Cells in the Future
20.11 Conclusion
Bibliography
Index
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