The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry E-Book
Pethuru Raj Chelliah
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: IEEE Press Series on Power Engineering
- Sprache: Englisch
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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Veröffentlichungsjahr: 2023
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
About the Authors
Foreword
Preface
1 A Perspective of the Oil and Gas Industry
1.1 Exploration and Production
1.2 Midstream Transportation
1.3 Downstream – Refining and Marketing
1.4 Meaning of Different Terms of Products Produced by the Oil and Gas Industry
1.5 Oil and Gas Pricing
1.6 A Note on Renewable Energy Sources
1.7 Environmental Impact
1.8 Uses of Hydrogen
Bibliography
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
2.6 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
2.10 AI Applications for the Oil and Gas (O&G) Industry
2.11 Better Decision‐Making Using AI
2.12 Cloud AI vs. Edge AI for the Oil and Gas Industry
2.13 AI Model Optimization Techniques
2.14 Conclusion
Bibliography
3 Artificial Intelligence for Sophisticated Applications in the Oil and Gas Industry
3.1 Introduction
3.2 Oil and Gas Industry
3.3 Artificial Intelligence
3.4 Lifecycle of Oil and Gas Industry
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
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
4.4 Crude Oil Production Process
4.5 Oil Exploration
4.6 Oil Extraction
4.7 Processing of Crude Oil
4.8 Overview of Refining
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
4.12 Non‐revenue Benefits of the Oil and Gas Industry
4.13 Conclusion
References
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
5.4 Technological Advancements in Midstream Sector
5.5 Midstream Sector Challenges
5.6 Conclusion
References
6 The Significance of the Industrial Internet of Things (IIoT) for the Oil and Gas Space
6.1 Overview of IIoT
6.2 Technical Innovators of Industrial Internet
6.3 IoT for Oil and Gas Sector
6.4 Rebellion of IoT in the Oil and Gas Sector
6.5 Oil and Gas Remote Monitoring Systems
6.6 Advantages of IIOT for the Oil and Gas Industry
6.7 Conclusion
Bibliography
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
7.5 Demarcating the Move Toward Edge AI
7.6 Why Edge AI Gains Momentum?
7.7 The Enablers of Edge AI
7.8 5G‐Advanced Communication
7.9 Why Edge AI is Being Pursued with Alacrity?
7.10 Edge AI Frameworks and Accelerators
7.11 Conclusion
Bibliography
8 AI‐Enabled Robots for Automating Oil and Gas Operations
8.1 Briefing the Impending Digital Era
8.2 Depicting the Digital Power
8.3 Robotics: The Use Cases
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
8.7 Conclusion
Bibliography
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
9.6 Drones Specialities and Successes
9.7 The Emergence of State‐of‐the‐Art Drones
9.8 Drones in the Oil and Gas Industry
9.9 AI‐Enabled Drone Services
9.10 AI Platforms for Drones
9.11 Conclusion
Bibliography
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
11 Illustrating the 5G Communication Capabilities for the Future of the Oil and Gas Industry
11.1 Introduction to 5G Communication
11.2 5G Architecture
11.3 Antennas For 5G
11.4 5G Use Cases
11.5 5G and Digitalization in Oil and Gas
11.6 5G Smart Monitoring Instruments
11.7 Conclusion
References
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
12.4 Microservices
12.5 Continuous Integration, Continuous Deployment (CI/CD)
12.6 Edge Computing
12.7 Conclusion
References
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
13.6 OPAF
13.7 OPC‐UA
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
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
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
16 Securing the Energy Industry with AI‐Powered Cybersecurity Solutions
16.1 Introduction
16.2 Energy Industry
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
16.17 Threat Modeling in AI
16.18 Incident Response
16.19 Fire Sale Scenario
16.20 Conclusion
References
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
17.3 The Challenges and Concerns of Artificial Intelligence
17.4 About the Need for AI Explainability
17.5 AI Explainability: The Problem It Solves
17.6 What is the AI Explainability Challenges?
17.7 The Importance of Explainable AI
17.8 The Importance of Model Interpretation
17.9 Briefing Feature Importance Scoring Methods
17.10 Local Interpretable Model‐agnostic Explanations (LIME)
17.11 SHAP Explainability Algorithm
17.12 Conclusion
Bibliography
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
18.3 About the Blockchain Technology
18.4 Blockchain‐Powered Use Cases for the Oil and Gas Industry
18.5 Blockchain for Improved Trust
18.6 Sensor‐Enabled Invoicing
18.7 Transportation Tracing
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
18.15 Tracking Carbon Footprint
18.16 Improved Pipeline Inspection
18.17 Other Miscellaneous Advantages of Blockchain
18.18 Blockchain Challenges
18.19 Conclusion
Bibliography
19 AI‐Inspired Digital Twins for the Oil and Gas Domain
19.1 How to Ensure Certainty Using DT for AI
19.2 Tools Needed to Develop Digital Twins
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
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
IEEE Press Series on Power and Energy Systems
End User License Agreement
List of Tables
Chapter 3
Table 1 Industry codes and its description for oil and gas.
Table 2 Lifecycle phase‐based AI applications.
Chapter 16
Table 1 Description of digitization in upstream.
Table 2 Description of midstream digitization.
Table 3 Types of downstream digitization.
Table 4 Differences between AI and ML.
Table 5 Steps in threat modeling.
Table 6 AI‐powered threat modeling.
List of Illustrations
Chapter 3
Figure 1 Lifecycle of the oil and gas industry.
Figure 2 AI based applications in the oil and gas industry.
Figure 3 Input of the user to AI Chatbot.
Figure 4 AI based optimized procurement.
Figure 5 ML‐based long‐term production prediction.
Figure 6 Well monitoring POV of AI.
Figure 7 Data for the training model to detect damage.
Figure 8 Optimizing production.
Chapter 4
Figure 1 Operations phases in the oil and gas industry.
Figure 2 Crude oil distillation/separation process.
Chapter 6
Figure 1 Overview of IIoT.
Figure 2 IIOT infrastructure.
Figure 3 Conceptual architecture of IIOT.
Figure 4 IIoT architecture viewpoints.
Figure 5 Supervisory control and data acquisition system.
Figure 6 Example architecture for the oil and gas industry.
Figure 7 Smart helmet in the oil & gas industry.
Chapter 11
Figure 1 5G network capabilities.
Figure 2 Architecture of E2E in 5G.
Figure 3 Network sliced architecture.
Figure 4 Circular patch antenna with millimeter wave Pin‐fed.
Figure 5 Magneto‐electric dipole antenna loaded with a dual‐polarized split‐...
Figure 6 Use cases of 5G.
Figure 7 IOT architecture layers.
Chapter 12
Figure 1 OS and container runtime.
Figure 2 Container representation.
Figure 3 Online shopping.
Figure 4 Saga pattern.
Figure 5 CI/CD pipeline.
Figure 6 OpenAPI and CI pipeline.
Figure 7 Network edges.
Figure 8 Edge architecture management.
Figure 9 Device lifecycle.
Figure 10 Kubernetes overview.
Figure 11 Service deployment.
Figure 12 Serverless lifecycle.
Figure 13 OpenWhisk.
Figure 14 Knative overview.
Chapter 13
Figure 1 ISA‐95 framework.
Figure 2 OPAS architecture.
Figure 3 OPA‐UA architectural organization.
Figure 4 Pub/sub system.
Figure 5 Redfish resource hierarchy.
Chapter 14
Figure 1 Digital twin concept.
Figure 2 Simulation flow.
Figure 3 Handover simulation.
Chapter 15
Figure 1 Hash function.
Figure 2 Certificate signing.
Figure 3 Boot chain.
Figure 4 FDO overview.
Figure 5 Azure onboarding.
Figure 6 Remote attestation.
Chapter 16
Figure 1 Flow of the various industries in collaboration.
Figure 2 Processes in non‐renewable energy disposition.
Figure 3 Types of renewable energy.
Figure 4 Supply chain of oil and gas.
Figure 5 Supply chain categories.
Figure 6 CIA triad.
Figure 7 Cybersecurity divisions.
Figure 8 ICS matrix of MITRE ATT&CK.
Figure 9 Cybersecurity framework stages.
Figure 10 Objectives of the cybersecurity framework.
Figure 11 NIST cybersecurity framework.
Figure 12 Zero‐day vulnerability cycle.
Figure 13 Machine learning methods.
Figure 14 Fusing AI into cybersecurity.
Figure 15 Incident response wheel.
Chapter 20
Figure 1 Hydrogen evolution reaction (HER).
Figure 2 Role of AI in hydrogen fuel systems.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
About the Authors
Foreword
Preface
Begin Reading
Index
IEEE Press Series on Power and Energy Systems
WILEY END USER LICENSE AGREEMENT
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IEEE Press445 Hoes LanePiscataway, NJ 08854
IEEE Press Editorial BoardSarah Spurgeon, Editor in Chief
Jón Atli BenediktssonAnjan BoseJames DuncanAmin MoenessDesineni Subbaram Naidu
Behzad RazaviJim LykeHai LiBrian Johnson
Jeffrey ReedDiomidis SpinellisAdam DrobotTom RobertazziAhmet Murat Tekalp
The Power of Artificial Intelligence for the Next‐Generation Oil and Gas Industry
Envisaging AI‐Inspired Intelligent Energy Systems and Environments
Pethuru Raj Chelliah
Edge AI DivisionReliance Jio Platforms LtdBangalore, India
Venkatraman Jayasankar
Leading Oil and Gas CompanyBangalore, India
Mats Agerstam
Principal Engineer, Intel’s Network and Edge GroupPortland, OR, USA
B. Sundaravadivazhagan
University of Technology and Applied Sciences ‐ Al MussanahOman
Robin Cyriac
Federation UniversityBrisbane‐CampusAustralia
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About the Authors
Pethuru Raj Chelliah – Working at Reliance Jio Platforms Ltd. (JPL) Bangalore. Previously He has worked at IBM Global Cloud Center of Excellence (CoE), Wipro Consulting Services (WCS), and Robert Bosch Corporate Research (CR). In total, he has gained more than 22 years of IT industry experience and 8 years of research experience. He finished the CSIR‐sponsored PhD degree at Anna University, Chennai, and continued with the UGC‐sponsored postdoctoral research in the Department of Computer Science and Automation, Indian Institute of Science (IISc), Bangalore. Thereafter, he was granted a couple of international research fellowships (Japan Society for the Promotion of Science (JSPS) and Japan Science and Technology Agency (JST)) to work as a research scientist for 3.5 years in two leading Japanese universities. He has been an ACM and IEEE professional member. He focus is on some of the digital transformation technologies such as the Internet of Things (IoT), artificial intelligence (AI), streaming data analytics, blockchain, digital twins, cloud‐native computing, edge and serverless computing, reliability engineering, microservices architecture (MSA), event‐driven architecture (EDA), and 5G/6G.
Venkatraman Jayasankar (Venkat) is an experienced IT professional with over 25 years of experience in different business domains like financial sector, manufacturing, healthcare, and energy. He has worked in multiple IT domains, industries, and technologies. He is a TOGAF (The Open Group Architecture Framework) certified architect and has worked extensively in architecture, designing solutions to complex business problems including IoT, edge computing, and asset management and digitalization. He has worked in different consulting organizations for clients in financial services, manufacturing, retail, healthcare, and energy. He is working for a leading oil and gas major as a Senior Principal Data Engineer focusing on all things related to data. His primary job is to make sure information and data are organized and governed for seamless analytics. He worked on architecture of digital twin, IoT, etc.
Mats Agerstam is an experienced system and software architect and researcher with over 20 years of professional experience in the information technology and software industry. Mats has a long background in security and wireless communication and protocols, particularly in technologies in the unlicensed spectrum such as WiFi, Bluetooth, and IEEE 802.15.4. He has been a key contributor in standardization bodies such as the Open Connectivity Foundation (OCF) and its open source reference implementation. He brings an extensive experience and expertise in the IoT space from multiple vertical segments such as industrial, retail, and enterprise. Mats holds a Master of Science degree in Information Technology Engineering from Uppsala University in Sweden and holds over 35 patents.
Dr. B. Sundaravadivazhagan is an experienced researcher and educator in the field of Information and Communication Engineering. He has more than 22 years of experience in teaching and research, and he earned his PhD in Information and Communication Engineering from Anna University in Chennai in 2016. He is a member of various professional bodies such as IEEE, ISACA, ISTE, and ACM, and has published over 40 research articles in SCI and Scopus journals. He has also served as a resource person, keynote speaker, and advisory committee member in more than 20 international and national conferences. He has received two research grants from the Ministry of Higher Education, Research and Innovation, The Research Council (TRC), Oman. His research interests include IoT, AI and machine learning, deep learning, cloud computing, networks and security, wireless networks, and MANET. He is a reputable journal editor and reviewer and serves on the Doctoral Committee in International Committee Member–Amrita University, Bangalore, as well as an Adjunct Faculty in Saveetha School of Engineering, Chennai.
Dr. Robin Cyriac is a distinguished computer science lecturer whose career has left an indelible mark on both academia and industry. With a PhD in Computer Science from a renowned institution, he embarked on a journey to share his profound knowledge and passion for the subject with eager minds. Dr. Robin’s engaging teaching style, coupled with his ability to simplify complex concepts, has earned him a reputation as a beloved educator among his students. Beyond his academic pursuits, Robin is known for his dedication to promoting diversity and inclusion in technology, striving to create an inclusive learning environment that empowers students from all backgrounds. He continues to inspire the next generation of computer scientists through his innovative curriculum and commitment to fostering critical thinking and problem‐solving skills in his students. His commitment to fostering innovation and critical thinking is evident through his engaging lectures and mentorship. Beyond the lecture halls, Robin has contributed significantly to research in IoT, security, and cloud technologies.
Foreword
As vice president of Architecture and Data for a major oil and gas company, I have had the privilege of witnessing firsthand the transformative power of artificial intelligence (AI) in the energy sector. The rapid evolution of digital technologies, combined with the explosion of data generated by the oil and gas industry, has presented a unique opportunity to leverage AI and advanced analytics to drive innovation and operational excellence.
Prior to joining Shell, I studied computer science and philosophy at university, where I became fascinated by the intersection of these two fields in the realm of AI. Despite the initial promise of AI, progress was hindered by the “frame problem,” which refers to the challenge of determining which (of the infinite number of) aspects of a changing environment should be considered when planning actions, a task that the human brain can handle well, but computers cannot. While there have been advancements in processing power, data handling, and algorithms, game‐changing AI technology remained elusive.
Today, the oil and gas industry is entering a new phase in which energy transition and digitalization are converging, creating an urgent need to embrace the advantages offered by AI. In their book, Pethuru Raj, Venkat, Mats, Sundar, and Robin provide an intriguing overview of the breadth and depth of AI and digital technology applications in the oil and gas industry. Their case studies and examples span the entire oil and gas value chain, from exploration and production to downstream asset management, showcasing how the applications of IoT, cloud, robotics, and digital twin technologies have a significant impact on safety and efficiency.
The book also provides insights into cybersecurity, which has become a growing concern as the integration of information technology (IT) and operational technology (OT) increases, and AI is used more widely across both domains. Anyone interested in these trends and the need for the oil and gas industry to transform and decarbonize while ensuring future energy security should read this book.
This book on AI in the oil and gas industry is a timely contribution to the growing body of knowledge in this area. The editors and contributors have brought together a diverse range of perspectives and expertise, reflecting the multidisciplinary nature of AI in the energy sector. From upstream exploration and production to downstream refining and managing assets, this book provides insights into how AI is transforming every aspect of the oil and gas value chain.
The case studies and examples presented in this book illustrate how AI is already delivering significant benefits to the industry, including improved efficiency, cost reduction, and enhanced safety. However, the potential of AI goes beyond operational optimization. As the energy transition accelerates, AI can help companies to decarbonize their operations, reduce their environmental footprint, and develop new business models that align with the changing energy landscape.
I commend the editors and contributors for their efforts in compiling this valuable resource for the oil and gas industry. I am confident that this book will serve as a catalyst for further innovation and collaboration in the application of AI to energy operations.
Nils Kappeyne
The Hague, The Netherlands24 April 2023
Preface
Business behemoths and start‐ups across the globe are keenly embracing the disruptive and transformative power of digital technologies and tools to produce and deliver strategically sound and sophisticated applications to their customers and consumers. Besides embarking on technology‐driven process optimization, enterprises are meticulously working in assimilating trend‐setting architectural patterns such as microservices and event‐driven architectures (MSA and EDA) to be agile and adaptive in their operations, offerings, and outputs. Further on, the infrastructure optimization primarily induced through the paradigm of cloud computing is another grandiose initiative of worldwide organizations to be right and relevant to their business partners, employees, and end‐users. Breakthrough digital technologies such as artificial intelligence (AI) are being leveraged by enterprising businesses to visualise and realize state‐of‐the‐art and sophisticated systems to enhance operational efficiency and the much‐desired customer delight.
With the widespread adoption of digitization and edge technologies, every kind of physical, mechanical, and electrical systems get digitized. With high‐bandwidth and highly reliable communication technologies, digitized entities connect with one another in the vicinity and also with cloud‐based software applications, enablement platforms, middleware solutions, and databases. Electronic devices are intrinsically being instrumented to be connected and cognitive in their activities and assignment.
Leading market analysts and researchers have predicted that there will be billions of connected devices and trillions of digitized elements on the planet earth in the years to unfurl. Resultantly, there will be a massive amount of multi‐structured digital data. It is therefore imperative to make sense out of digital data mountains. There are integrated data analytics platforms to collect, cleanse, and crunch digital data to emit actionable insights. For any enterprise to march ahead in the right direction with all the confidence and clarity, all kinds of internal and external data have to be meticulously gleaned and subjected to a variety of deeper investigations to discover hidden knowledge. The discovered knowledge then gets disseminated to the concerned applications and devices to exhibit intelligent behavior.
Artificial intelligence (AI) is the latest popular paradigm to transition data into information and into knowledge. AI is being methodically supported in its vision of making intelligent systems by a host of machine and deep learning algorithms and models. Precisely speaking, AI is the flexible and futuristic paradigm for making sense out of data heaps. In the digital era, it is paramount and pertinent for product, solution, and service providers to extract actionable insights out of data to envisage next‐generation offerings to retain their customers and to attract fresh consumers. AI has laid down a stimulating and sparkling foundation for worldwide enterprises to explore fresh avenues to enhance their revenues.
This book is dedicated to articulate and accentuate how the growing power of AI helps immensely in transforming the oil and gas industry to meet up the fast‐emerging and evolving business, technical, user, and sustainability requirements. The book chapters are prepared and presented to understand the challenges and concerns of the oil and gas industry and how they are being addressed by leveraging the distinct capabilities of AI algorithms, models, frameworks, and toolsets.
The AI technology is being positioned and primed as the path‐breaking paradigm for the entire human society as it has the innate wherewithal to visualize and realize plenty of intelligent systems and environments across industry verticals. AI is becoming famous and feverish as it has the required competency to replicate human brain capabilities into our everyday devices, business workloads, and IT services. AI‐attached software products, solutions, and services can inherently exhibit data‐driven insights and insights‐driven decisions. There is less intervention, interpretation, and involvement of human beings in operating and managing AI‐inspired systems situated in our living, relaxing, and working environments. The AI power grows and glows as there are many technological innovations and disruptions in the IT space. Essentially, we are tending toward the big data era with the exponential growth of different and distributed data sources.
Besides articulating and accentuating the AI power in strategically and significantly empowering the oil and gas industry, we have prepared and presented several chapters leveraging other related technologies such as the Industrial Internet of Things (IIoT), cyber‐physical systems (CPS), digital twins, blockchain, edge computing, 5G communication, etc. Further on, we have dealt with new fuels and how they are going to sustain the human society. How intelligent drones and robots automate, accelerate, and augment various operations such as exploration, extraction, transportation, refinement, and retailing are vividly illustrated in this book. Finally, we have incorporated a chapter on explainable AI (XA) in order to insist the importance of trust and transparency in AI decisions, recommendations, predictions, and outcomes. One chapter exclusively talks about the blockchain technology, which is being portrayed as the way forward to ensure the tightest security for IoT edge devices and data.
In this book, we would like to focus on the oil and gas industry and how to make it smarter in its operations, outputs, and offerings through the leverage of the distinct advancements happening in the AI space. There are several problems and needs for leveraging the AI power to drastically and deftly empower oil and gas systems. The goal is to arrive at the digitally transformed oil and gas industry. This book will start with the challenges and concerns of the oil and gas industry. Then we will focus on the unique capabilities of the AI paradigm. Finally, we dig deeper and deal with how AI comes handy in empowering the oil and gas industry significantly. This book is comprehensive yet compact in illustrating how digitization and digitalization technologies blend together to bring forth a bevy of innovations, disruptions, and transformations for the oil and gas industry.
Pethuru Raj ChelliahEdge AI, Reliance Jio Platforms Ltd.Bangalore24 April 2023
1A Perspective of the Oil and Gas Industry
Oil was always used for commercial purposes like lighting and heating for a long time. In 1859, the first commercial oil well was drilled for the purpose of finding oil and using it for industrial purposes
What does crude oil contain?
Crude oil is a naturally occurring fossil fuel – meaning it comes from the remains of dead organisms like algae
Crude oil is made up of a mixture of hydrocarbons – hydrogen and carbon atoms, methane, ethane, propane, and butane exist as gases, while pentane exists as liquids
An oil well predominantly produces crude oil, which is mixed a bit with natural gas, mainly methane
A gas well produces natural gas predominantly with very little crude
Crude oil is often referred to as petroleum. This is because petroleum includes both the unrefined crude oil as well as refined petroleum products.
The oil and gas industry has the following stages of oil extraction:
Upstream – Exploration and Production
Midstream – Transportation
Downstream – Refining and Marketing
1.1 Exploration and Production
Oil and gas exploration encompasses the processes and methods involved in locating potential sites for oil and gas drilling and extraction. Early oil and gas explorers relied upon surface signs like natural oil seeps, but developments in science and technology have made oil and gas exploration more efficient.
In the past, surface features such as tar seeps or marks provided initial clues to the location of shallow hydrocarbon deposits. Today, accurate geological surveys are conducted using various means for exploration. Rock or sand surveys are done to see if there can be any possible deposits under the surface. Seismic surveys are also conducted by geologists to find oil deposits under the surface. If a site seems to have oil, an exploratory well is drilled and if there are deposits worth of value, then full‐fledged development wells are drilled to extract the oil
Once the prospective reserve is found, companies will start drilling using mobile offshore drilling units (MODU). Once the drilling units find oil, the company will replace it with a more permanent oil production rig to capture oil.
Exploration is of high risk and expensive. The cost of a basic exploration, such as one that involves deep seismic studies, can cost $5–$20 million per exploration site, and in some cases, much more. However, when an exploration site is successful and oil and gas extraction is productive, exploration costs are recovered and are significantly less in comparison to other production costs.
Proven reserves measure the extent to which a company thinks it can produce economically recoverable oil and gas in place, as of a certain point in time, using existing technology.
Once a company identifies where oil or gas is located, plans begin for drilling, and the drilling methods vary depending on the type of oil or gas and the geology of the location.
To drill a well, it is necessary to simultaneously carry out the following drilling process.
Crush the rocks under the earth to small pieces so that liquid can flow and the drill can travel down to get to the oil or hydrocarbons
Remove the rock debris and continue drilling
To make sure the holes do not cave in, preventing the drilling
Prevent the fluids contained in the drilled formations from entering the well.
This can be achieved by using rotary drilling rigs, which are the ones operating today in the field of hydrocarbon exploration and production. The drills have a conveyor belt which continuously removes the debris that the drill digs out. These modern drills operate with great efficiency, but they can also cause damages to the areas around the drill site if they are not operated according to the conditions. The specialized equipment and complex technology make drilling of oil wells extremely expensive and of high risk.
1.1.1 Onshore
In onshore drilling facilities, the wells are grouped together in a field, ranging from half an acre per well for heavy crude oil to 80 acres per well for natural gas. The group of wells are connected by steel tubes, which send the oil and gas to a production and processing facility where the oil and gas are treated through a chemical and heating process. Onshore production companies can turn on and off rigs more easily than offshore rigs to respond to market conditions due to the need that offshore rigs need to be visited by engineers to inspect and close.
1.1.2 Offshore
Offshore drilling uses a single platform that is either fixed (bottom supported) or mobile (floating secured with anchors). Offshore drilling is more expensive than onshore drilling, and fixed rigs are more expensive than mobile rigs. Most production facilities are located on coastal shores near offshore rigs to reduce expenses and safety issues.
1.1.3 Hydraulic Fracturing
Fracking, or hydraulic fracturing, is a technique where a high‐pressure liquid is injected to form cracks or fractures on the rocks to extract oil or gas. The use of fracking has led to recovering gas, and the oil extracted by this method is called shale oil.
Once a prospective reserve is found, companies will drill highly regulated exploration wells with Mobile Offshore Drilling Units (MODUs).
The MODU’s job is to drill down into the ocean floor and find oil and natural gas reserves.
Once a well is found, the company switches to a more viable model of production. All the legal entities are created, and in many cases the drill that was used for initial explorations will be used for the production as well. The part of the well that allows the drill bit to drill without interruptions, like a pipe or casing, is called riser. A drilling riser may terminate at the sea floor or may extend slightly into the earth to prevent water infiltration.
Source:EnggCyclopedia.com.
1.2 Midstream Transportation
The midstream sector involves the transportation (by pipeline, rail, barge, oil tanker, or truck), storage, and wholesale marketing of crude or refined petroleum products. Pipelines and other transport systems can be used to transport crude oil from production sites to refineries and deliver the various refined products to downstream distributors. Natural gas pipeline networks aggregate gas from natural gas purification plants and deliver it to downstream customers, such as local utilities.
Midstream activity starts after oil and gas is extracted. Once the gas is extracted from the wells, it must be refined. Midstream activity consists of the different process steps to transport and refine the oil.
Modes of transportation include the following:
Oil tankers
: A tank vessel as one that is constructed or adapted to carry oil or hazardous material in bulk as cargo or cargo residue – as defined by the US Coast Guard. There are various types of tankers like oil, parcel tanker, combination, and barges. Many oil companies work on upstream, downstream, midstream and hence considered integrated. In many countries, midstream business does not exist as a separate business and is combined with upstream business.
LNG tankers
: High‐pressure possibility of explosions make it difficult to transport natural gas. For this reason, natural gas is liquefied at extremely low temperatures and transported as LNG via liquefied natural gas (LNG) tankers. LNG tankers are specially designed with double hulls to allow extra ballast water because LNG is lighter than gasoline.
Pipelines
: Pipelines can do the work of transporting oil and gas to gathering systems (wellhead to processing facilities), transmission lines (supply areas to markets), or distribution pipelines (most commonly to transport natural gas to medium or small consumer units). Pipelines play a very critical role in the transportation process because most of the oil moves through pipelines for at least part of the route. After the crude oil is separated from natural gas, pipelines transport the oil to another carrier or directly to a refinery. The only challenge is laying of pipelines to get the oil transported.
Strategic planning involves determining the shortest and most economical routes where pipelines are built, the number of pumping stations and natural gas compression stations along the line, and terminal storage facilities so that oil from almost any field can be shipped to any refinery on demand.
Railroad/tank trucks
: Historically, before the introduction of pipelines, railroads were used to transport petroleum. Today, railroads compete with pipelines. The existing railroad infrastructure creates a more flexible, alternative route when pipelines are at capacity. For instance, many a times, a railroad exists; however, laying a pipeline becomes a huge challenge. The railroad would have been laid before the actual development or roadblocks came up due to growth in the area, and it would now become a challenge to create new pipelines. Also, there are many chances of pipelines breaking or getting damaged. Reaching a damaged pipeline maybe a difficult task for engineers, and it is time‐consuming. However, transporting oil by railroads has its own set of challenges.
Storage of oil and natural gas helps smooth out supply and demand discrepancies. Companies store more when the prices are lower than they would like and withdraw when prices are high. The oil storage is also done by different countries and is known as strategic oil reserves. Strategic oil reserves usually help a country when there are issues in oil supply due to natural calamities, man‐made issues, etc. The cheapest storage method is underground tanks, such as depleted reservoirs. This method is primarily used for natural gas. Aboveground tanks are used for crude and refined oil, finished oil products, and natural gas. At retail locations, like gas stations, tanks are stored underground for safety reasons.
1.3 Downstream – Refining and Marketing
Downstream covers refining and marketing, or in some organizations it is trading and supply. The goal of refining is straightforward, to take crude oil, which is virtually unusable in its natural state, and transform it into petroleum products used for a variety of purposes and then sell it. Downstream depends on upstream for a steady supply of crude oil to help refine the oil and supply downstream.
One of the key processes involved in refining depending on the product needed is hydrotreating.
1.3.1 Hydrotreating
Hydrotreating or hydrodesulfurization refers to a set of operations that remove sulfur and other impurities. During hydrotreating, crude oil is made to react with hydrogen in the presence of a catalyst at relatively high temperatures and moderate pressure.
Hydrotreating helps petroleum refineries transform crude oil into useful fuels and products while satisfying government safety requirements. One of the most common issues is nickel catalyst poisoning by sulfur, which is present in the crude form extracted from catalytic beds.
Marketing is the wholesale and retail distribution of refined petroleum products to business, industry, government, and public consumers. Marketing is also known as trading and supply in some organizations.
Gasoline service stations handle the bulk of public consumer sales, and oil companies sell their petroleum products directly to factories, power plants, and transportation‐related industries. Natural gas sales are almost evenly divided between industrial consumers, electrical providers, and residential and commercial heating.
In simpler terms, upstream operations include oil and gas production, midstream includes storage and transportation, and downstream includes distribution and retail outlets.
1.4 Meaning of Different Terms of Products Produced by the Oil and Gas Industry
1.4.1 Natural Gas
Natural gas is a fossil fuel. Natural gas contains many different compounds. The components of natural gas include methane, natural gas liquids (NGLs, which are also hydrocarbon gas liquids), and nonhydrocarbon gases, such as carbon dioxide and water vapor. We use natural gas as a fuel and to produce materials and chemicals. Natural gas is the cleanest burning hydrocarbon. A natural gas‐powered station takes lesser time to start and stop compared to a coal‐powered station. The main natural gases are methane, ethane, butane, and propane.
It is used for many things, especially in the home. Some common examples are as follows:
Home heating through furnaces
Warming water in water heaters
Cooking food on barbecues and gas‐burning stoves
Operating gas fired fireplaces
1.4.2 Extraction
Natural gas, being an unconventional gas, must be extracted from deeper areas below the surface of the Earth. It consists of fracking or fracturing the rocks, passing water in high pressure to simulate the gas to flow via the pipes to the surface.
1.4.3 Advantages and Disadvantages
Natural gas has a high energy density and can be used flexibly for multiple applications, which make it a popular fuel. People advocating using natural gas often point to it as the cleanest burning fossil fuel. Even as a cleanest burning fossil fuel, natural gas is still composed of hydrocarbons, and burning it releases CO2 and other pollutants (NOx being a problem specifically). Natural gas use is often an improvement over that of coal; however, its combustion still contributes to air pollution and climate change.
With advances in fracking techniques, natural gas reserves are expected to last a long time.
Natural gas power plants generate electricity by burning natural gas as their fuel. There are many types of natural gas power plants which all generate electricity but serve different purposes. All natural gas plants use a gas turbine; natural gas is added, along with a stream of air, which combusts and expands through this turbine, causing a generator to spin a magnet, producing electricity.
Natural gas power plants are cheap and quick to build. They also have very high thermodynamic efficiencies compared to other power plants. Burning of natural gas produces fewer pollutants like NOx, SOx, and particulate matter than coal and oil.
Despite the improved air quality, natural gas plants significantly contribute to climate change, and that contribution is increasing. Natural gas power plants produce considerable carbon dioxide, although less than coal plants do. On the other hand, the process of getting natural gas from where it is mined to the power plants leads to considerable release of methane (natural gas leaks into the atmosphere). If natural gas plants are used to produce electricity, their emissions will continue to warm the planet in dangerous ways.
1.4.4 Types
The use of natural gas accounts for around 23% of the world's electricity generation.
This is second only to coal, and the fraction that is natural gas is expected to grow in coming years. This means that the contribution of natural gas to climate change will continue to increase.
1.4.5 Types of Natural Gas Deposits
Natural gas can be contained in a variety of different types of deposits that must be accessed if the natural gas is to be used. According to the Canadian Association of Petroleum Producers (CAPP), Canada has a natural gas reserve of between 700 and 1300 trillion cubic feet. While a little over 15% of that natural gas has been recovered, the rest is contained in four types of deposits: conventional and unconventional deposits: shale gas deposit, tight gas deposit, and coal bed methane.
Natural gas has been extracted from conventional natural gas deposits for a long time; the unconventional resources are resources that are being extracted using substantially new techniques.
1.4.6 Conventional Natural Gas Deposits
Conventional resources are gas contained within relatively porous rock, and they are the most easily mined. While hydraulic fracturing has allowed for more expansive access to these deposits, they can be mined without its use.
1.4.7 Coal Bed Methane
Coal bed methane is natural gas consisting mostly of methane, which is trapped inside coal deposits under the surface. This is extracted while the coal is being mined, as the diminishing pressure in the coal seam allows the gas to flow out of the seam and into a wellbore, where it is extracted.
1.4.8 Shale Gas
Shale gas is natural gas found inside a fine‐grained sedimentary rock called shale. Shale is porous (there are lots of tiny spaces inside it), but it is non‐permeable, which means the gas cannot flow through it. Shale gas requires the use of hydraulic fracturing for extraction.
1.4.9 Tight Gas
Tight gas is like shale gas, in that it is trapped inside a porous, non‐permeable reservoir rock. The only differentiation between the two is that the term tight gas includes natural gas trapped inside reservoir rocks that are not shale.
1.4.10 Environmental Impacts of Natural Gas
1.4.10.1 Global Warming Emissions
Natural gas is a fossil fuel at the end of the day, which means there will be environmental impacts starting from drilling for gas, emissions during combustion, etc., though the global warming emissions from its combustion are much lower than those from coal or oil.
Natural gas emits 50–60% less greenhouse gases when combusted in a new, natural gas power plant compared with emissions from a typical new coal plant. Considering only tailpipe emissions, natural gas also emits 15–20% less heat‐trapping gases than gasoline when burned in today’s typical vehicles.
Drilling and extraction of natural gas from wells and its transportation in pipelines results in the leakage of methane, the primary component of natural gas, which is many times more stronger than CO2 at trapping heat, which results in environmental hazards.
The emissions created by natural gas usually depend on the assumed leakage of methane, which is eventually a major reason for trapping the heat. One study found that methane losses must be kept below 3.2% for natural gas power plants to have lower life cycle emissions than new coal plants over short time frames of 20 years or lesser. This means implementation of more tighter controls and better systems to make sure the losses are monitored and reduced as per need. Similarly, vehicles burning natural gases must also keep methane losses much below 1% and 1.6% compared with those burning diesel fuel and gasoline, respectively. Technologies are available to reduce much of the leaking methane, but deploying such technology would require new policies and investments.
1.4.10.2 Air Pollution
Combustion of natural gas produces negligible amounts of sulfur, mercury, and other particulate matter. Burning natural gas does produce nitrogen oxides (NOx), which are precursors to smog, but at lower levels than gasoline and diesel used for motor vehicles. DOE analyses indicate that every 10,000 U.S. homes powered with natural gas instead of coal avoids annual emissions of 1900 tons of NOx, 3900 tons of SO2, and 5,200 tons of particulate matter. Reductions in these emissions translate into public health benefits.
However, despite these benefits, unconventional gas development can affect local and regional air quality. Some areas where drilling occurs have experienced increases in concentrations of hazardous air pollutants. Exposure to elevated levels of these air pollutants can lead to adverse health outcomes, including respiratory problems, cardiovascular diseases, and cancer. Another study found that residents living less than half a mile from gas well sites were at greater risk of health effects from air pollution from natural gas development than those living farther from the well sites.
1.4.10.3 Land Use and Wildlife
The construction and land required for oil and gas drilling can alter land use and harm local ecosystems by causing erosion and fragmenting wildlife habitats and migration patterns. When oil and gas operators clear a site to build a well pad, pipelines, and access roads, the construction process can cause erosion of dirt, minerals, and other harmful pollutants into nearby streams.
1.4.10.4 Water Use and Pollution
Unconventional oil and gas development can produce health risks by contaminating water and making people around fall sick. The hazardous chemicals which were once deep under the surface are drilled, and some of them get mixed with the water, causing serious contamination of water resources. There are many instances where clean water gets completely contaminated by chemicals as a result of deep drilling for oil and gas. Radioactive chemicals and methane can pose major health risks when leaked into the water supplies by carelessness or leaks or both.
The large volumes of water used in unconventional oil and gas development also raise water availability concerns in some communities.
1.4.10.5 Groundwater
There have been multiple documented instances of ground water getting contaminated with fracking or other fluids that are used to explore shale oil. One of the key challenges when ground water gets contaminated is that there are little options to clean up the mess that is created, and there must be a massive people/livestock relocation due to safety concerns. One major cause of gas contamination is improperly constructed or failing wells that allow gas to leak from the well into groundwater.
Another potential avenue for groundwater contamination is natural or man‐made fractures in the subsurface, which could allow stray gas to move directly between an oil and gas formation and groundwater supplies.
In addition to gases, groundwater can become contaminated with hydraulic fracturing fluid. In several cases, groundwater was contaminated from surface leaks and spills of fracturing fluid.
1.4.10.6 Surface Water
Unconventional oil and gas development also poses contamination risks to surface waters through spills and leaks of chemical additives, spills and leaks of diesel or other fluids from equipment on‐site, and leaks of wastewater from facilities for storage, treatment, and disposal. Unlike groundwater contamination risks, surface water contamination risks are mostly related to land management and to on‐ and off‐site chemical and wastewater management.
There has been more than 1000 chemical additives identified that are used for hydraulic fracturing, including acids (notably hydrochloric acid), bactericides, scale removers, and friction‐reducing agents. Large quantities – tens of thousands of gallons for each well – of the chemical additives are trucked to and stored on a well pad. If not managed properly, the chemicals could leak or spill out of faulty storage containers or during transport.
Drilling debris, diesel, and other material/fluids that are used when drilling can also spill at the surface, creating temporary or permanent damage. Improper management of flowback or produced wastewater can cause leaks and spills. There is also risk to surface water contamination from improper disposal. This could lead to areas around the drill site getting impacted: earthquakes around the drill site, water issues, and agriculture getting impacted and many more.
1.4.10.7 Water Use
The growth of hydraulic fracturing and its use of huge volumes of water per well may strain local ground and surface water supplies, particularly in water‐scarce areas. The amount of water used for hydraulically fracturing a well can vary because of differences in formation geology, well construction, and the type of hydraulic fracturing process used The EPA estimates that 70–140 billion gallons of water were used in 2011 for fracturing an estimated 35,000 wells. This water is not sea water or ground water, which can get years to be replenished. Unlike other energy‐related water withdrawals, which are commonly returned to rivers and lakes, most of the water used for unconventional oil and gas development is not recoverable. Depending on the type of well along with its depth and location, a single well with horizontal drilling can require 3–12 million gallons of water when it is first fractured – dozens of times more than what is used in conventional vertical wells. Similar vast volumes of water are needed each time a well undergoes a “work over,” or additional fracturing later in its life to maintain well pressure and gas production.
1.4.11 The Future of Natural Gas
Replacing coal with natural gas in the electricity sector is not an effective long‐term climate strategy.
