book

AI and Digital Technology for Oil and Gas Fields

Name: AI and Digital Technology for Oil and Gas Fields
Author: Niladri Kumar Mitra
ISBN: 9781000857542

by Niladri Kumar Mitra

October 2024

Intermediate to advanced

258 pages

8h 15m

English

CRC Press

Read now

Unlock full access

Cover
Half Title
Title Page
Copyright Page
Table of Contents
About the Author
Preface
Acknowledgments
Chapter 1 The Growing Role of Artificial Intelligence in the Oil and Gas Industry
1.1 History and Trends of Future Digital Technology1.2 AI Techniques1.2.1 Applications of AI1.2.2 Types of AI Systems1.3 Hardware for AI1.4 Big Data and Data Analytics for Oil Wells1.4.1 Long-Reach Digital Data1.5 Data Filtering and Conditioning Interference from Signals1.5.1 Amplifying Transactions on the Chain1.5.2 Augmenting Collaboration1.6 Case-Based ReasoningBibliography
Chapter 2 Artificial Intelligence Agent System
2.1 Introduction2.1.1 Multi-Agent Systems2.1.2 Autonomy2.1.3 Reactivity2.1.4 Reactiveness2.1.5 Collaborative Agents2.1.6 Interface Agents2.1.7 Mobile Agents2.1.8 Information Agents2.1.9 Reactive Agents2.1.10 Hybrid Agents2.1.11 Heterogeneous Agents2.1.12 Responsible Agents2.1.13 AI Components in Multi-Agent Systems2.1.14 AOP Components and Modalities2.1.15 Blackboard Architecture2.2 User Interface2.2.1 Computer Interface Specification2.2.2 Human Factors2.2.3 Types of Human User Interface2.2.4 Classification of AIS Services2.3 Knowledge-Based Systems2.3.1 Structure of an Expert System2.4 Knowledge Discovery of Data and Web Mining2.4.1 KDD2.4.2 Business2.4.3 Industries2.4.4 Web Mining2.4.5 KDD Methods2.4.6 Data Cleaning and Pre-Processing2.4.7 Data Reduction and Transformation2.4.8 Data Mining Task2.4.9 Solution Strategy2.4.10 Interpretation2.4.11 Evaluation2.5 Web Technology, Semantic Web, and Knowledge Management2.5.1 Semantic Web Services (SWS) Framework2.5.2 Knowledge Management2.6 Data Filtering from Signals2.6.1 Data Analysis Adds Value to Content Filtering2.6.2 Choosing the Right Type of Filter2.6.3 Anti-Aliasing Filter2.6.4 Band-Pass and Band-Stop Filter2.6.5 Post-Collection FilteringBibliography

Chapter 3 Artificial Intelligence and Automatic Control
3.1 Introduction3.2 Concept of Automation3.2.1 Numerical Control3.2.2 Computer-Aided Manufacturing3.2.3 Flexible Manufacturing Systems (FMS)3.2.4 Industrial Robots3.3 Differences between AI and Automation3.4 Major Components of AI in Automation3.5 Applications of AI in Automation3.6 Role of AI in Social Media3.7 Enhancement of Human Condition with the Participation of AI3.8 Comparative Analysis and a Higher Level of Intellectual Control3.8.1 General Problems of Control Systems Intellectualization3.9 The Large-Sized Structure of Control Science and Technologies3.9.1 Neural Network Control3.10 Change in Digital Oilfield Management (DOF)3.11 Layers of Digital Oilfield3.12 Production Data Management System3.13 Integrated Asset ManagementBibliography
Chapter 4 The Components of Trusted Artificial Intelligence
4.1 Introduction4.1.1 Trusted AI4.1.2 Application of AI Systems4.2 Cognitive Abilities4.2.1 Database4.2.2 Hardware4.2.3 Framework4.2.4 Role of APIs for AI Services4.3 Philosophy of AI4.3.1 Goals of Any AI System4.3.2 Contribution of AI Systems4.3.3 Programming with and without AI4.3.4 Building Four Critical Components of a Trusted AI System4.3.5 The Four Pillars of Trusted AI4.3.6 Fairness4.3.7 Explainability4.3.8 Robustness4.3.9 Lineage4.4 A Factsheet for AI Systems4.5 Testing Methodology4.6 Key Technologies of AI4.7 Infrastructure Layer4.7.1 Basic Hardware4.7.2 Framework of Algorithm4.7.3 Algorithm Layer4.8 Component Analysis4.8.1 Extracting Criteria and Weighting Criteria4.9 Finding ComponentsBibliography
Chapter 5 Industrial Internet of Things (IIoT)
5.1 Introduction5.2 Background of IIoT5.3 Evolution of IIoT5.3.1 Industry 1.0 (1784)5.3.2 Industry 2.0 (1870)5.3.3 Industry 3.0 (1969)5.3.4 Industry 4.0 (2010)5.4 Main Architecture of IIoT5.4.1 Three- and Five-Layer Architectures5.4.2 Cloud- and Fog-Based Architectures5.5 Social IoT5.5.1 Basic Components5.5.2 Representative Architecture of IoT5.5.3 The Four Elements of the Industrial Internet5.6 Standards and Frameworks of IoT5.7 Applications in the Industry5.8 IIoT for the Oil and Gas IndustryBibliography
Chapter 6 Artificial Intelligence and Robotics
6.1 Introduction6.2 Singularity and Super-Intelligence6.3 Steps for Designing an AI System6.4 Basics of Robotics6.5 Different Branches of Studies Involved in the Development of Robotics6.5.1 Major Role of Robots in Human Life6.5.2 Assembling a Robot6.5.3 Packaging Industry Works by Robots6.6 Open Source Robotics6.7 Robotics and AI6.7.1 Robotics6.7.2 Basic Type of Cobot6.7.3 Artificially Intelligent Collaborative Robots6.7.4 Industrial Robots6.8 Pure AI for Alpha Go6.9 AI-Powered Robots6.9.1 Application in Robotics and AI6.9.2 Software Robots6.9.3 Robotic Process Automation (RPA)6.9.4 Chatbot6.9.5 Role of RPA in Operations6.9.6 RPA Actual Deployment6.9.7 Harnessing AI Combined with RPA6.9.8 Effect on Society6.10 A Typical Mechanical Design of A Robotic Arm—A Case Study6.10.1 Functionality of a Robotic Arm6.10.2 Reliability of a Robotic Arm Design6.10.3 Robotic Motion Range and Speed6.10.4 Payload Consideration for a Robot6.10.5 Reach of the Robot6.10.6 Axes of the Robot6.10.7 Design of a Robot—Part 16.11 Robotic Process Automation-2.06.12 HyperautomationBibliography
Chapter 7 Machine Learning and Artificial Intelligence
7.1 Introduction7.2 Data Science and AI7.3 Machine Learning7.4 Decomposition of Machine Learning7.5 Machine Learning Process and Application Software7.5.1 Machine Learning Operating Software7.5.2 Advantages of Python7.5.3 AI and Python7.5.4 Decoding Python Alongside AI7.5.5 Python Libraries for General AI7.5.6 Python for Machine Language7.5.7 Python Libraries for Natural Language and Text Processing7.5.8 Comparison between Python and C++ for AI7.5.9 Case Study7.6 Machine Learning Classification7.6.1 Machine Learning Categories7.6.2 Reinforcement Learning7.6.3 Deep Learning Algorithms7.6.4 Artificial Neural Network (ANN)7.6.5 BP Neural Network7.6.6 Convolutional Neural Network7.6.7 Recurrent Neural Network7.7 Transfer Learning7.8 Relation to AI7.9 Generative Adversarial Network (GAN)7.10 Data Mining7.10.1 Relationship with Data Mining7.11 Relation to Optimization7.12 Relation with Statistics7.13 Supervised Machine Learning7.13.1 Linear Regression7.14 Gradient Descent Algorithm7.14.1 Batch Gradient Descent7.14.2 Mini-Batch Gradient Descent7.14.3 Stochastic Gradient Descent (SGD)7.14.4 Logistic Regression7.14.5 Representation Used for Logistic Regression7.15 Case Study-17.16 Case Study-27.16.1 Before7.16.2 After7.16.3 Results7.16.4 Approach7.16.5 Technical Details7.17 Case Study-37.17.1 Before7.17.2 After7.17.3 Approach7.17.4 Technical DetailsBibliography
Chapter 8 Intelligent Drilling and Well Completion
8.1 Introduction8.1.1 Role of Digital Technology in Drilling Planning and Operation8.1.2 Digital Drilling Application8.1.3 Service Provider’s Dilemma8.1.4 Digital Drilling8.1.5 Continuous Improvement and Sustainable Performance8.1.6 New High-Tech Drilling Planning8.2 Well Completion8.2.1 Perforations8.2.2 Definition and Principle of Intelligent Well Technology8.2.3 An Intelligent Well Completion System8.2.4 Design Plan for the Development of Intelligent Well Technology8.3 Inflow Control Devices8.3.1 Fundamental ICD Design Equations8.3.2 Typical Type of ICD (Tesla Diode Model)8.3.3 CFD Models for ICD Design8.3.4 Discretization Model of ICDs8.3.5 Type of Fluid Dynamics of ICDs8.3.6 Laboratory Observation on CFD Models8.4 Inflow Control Valve Design8.4.1 Ideal Characteristics of Performance Criteria of ICDs8.4.2 Limitations of Current Criteria of Selection8.4.3 Autonomous Inflow Control Valves8.5 Composition of Intelligent Well Technology8.5.1 Downhole Information Detection and Acquisition System8.5.2 Data Information Transmission System8.5.3 Up-Hole Data Analysis System8.5.4 Characteristics of Intelligent Well Technology8.5.5 Surface System Analysis and Decision-Making and Closed-Loop Control of Downhole Tools8.5.6 Surface Readout (SRO) Data from Bottom-Hole Pressure Transmitter8.5.7 Surface Readout Pressure Transmitter and Downhole Recorder8.5.8 Downhole Swell Packer8.5.9 Bottom-Hole Pressure and Temperature Transmitter8.5.10 Bottom-Hole Sonic Gauge8.5.11 Sonar-Based Flow Measurement System8.5.12 Pressure Pulse Technology8.5.13 Modular-Based Multiphase and Wet Gas Flow Meter8.6 Monitoring Technology, Downhole Reservoir Sensors8.7 Advantage of Intelligent Well Completion8.7.1 Limitations of Intelligent Well Technology8.8 Some of the Important Development Status Quo of Intelligent Well Technology8.8.1 Monitoring Technology8.8.2 Data Processing Technology8.8.3 Application of Intelligent Well Completion System8.8.4 Reservoir Characterization for Intelligent Well Completion8.8.5 Overview of Reservoir Engineering Model for IWC8.8.6 Maximizing Reserves Recovery8.8.7 Reservoir Modeling for Horizontal Well: Example 18.8.8 Field Problem Description: Example 2Bibliography
Chapter 9 Reservoir Management and iCloud Storage
9.1 The Basic Concept of Reservoir Modeling9.2 Static Modeling9.2.1 Reservoir Simulation9.2.2 Map Statistics9.2.3 Kriging9.2.4 Simulation9.2.5 Geostatistics9.2.6 Basic Concept of Reservoir Modeling9.2.7 Reservoir Drives9.3 Reservoir Heterogeneity9.4 Reservoir Simulation and iCloud Storage9.4.1 iCloud Backup9.4.2 Reservoir Simulation Data Storage Moves to the Cloud9.5 A Modern Reservoir Simulator Interface: Case Study-19.5.1 Scalability Challenges9.5.2 Case Study-29.5.3 Linear Scaling for Reservoir Simulations9.6 A Deliberation about Schlumberger’s New DELFI Cognitive E&P Environment Enabling Faster Turnaround in the Exploration-to-Production Life Cycle9.6.1 A Secure, Cloud-Based Environment9.6.2 Secure9.6.3 Collaborative9.6.4 Open9.6.5 Cognitive Technology9.6.6 Live Project9.6.7 Synopsis of Use Cases of the Valuable Benefits to Various Customers Using Schlumberger’s Digital Technologies (DELFI) Software9.7 Role of Cloud Business9.7.1 Inherently Limited Scalability of Single-Provider Clouds9.7.2 Lack of Interoperability among Cloud Providers9.7.3 No Built-In Business Service Management Support9.8 The Reservoir ApproachBibliography
Chapter 10 Distributed Digital Control Systems for Oil and Gas Operations
10.1 Evolution of Process Control Operations10.1.1 Background10.1.2 The Chronological Development of Networks10.1.3 Some of the Significant Development of the Network System in the 1990s10.2 Basic Elements of Distributed Control Systems10.2.1 Elements of DCS10.2.2 Distributed Controller or Local Control Unit10.2.3 Operating Station10.2.4 Communication Media and Protocol10.3 Architecture of DCS10.4 Important Features of Digital Control Systems10.4.1 Handling Complex Processes10.4.2 Digital Control System Redundancy10.4.3 DCS Architecture10.4.4 To Handle Complex Processes10.4.5 More Sophisticated Human–Machine Interface10.4.6 Scalable Platform10.4.7 System Security10.4.8 Application of Distributed Control Systems10.5 Modern DCS Systems10.5.1 Embedded Web-Servers10.5.2 Five Subsystems of Distributed Control Systems10.5.3 Distributed Control System Checkout10.6 Role of the Fieldbus System10.6.1 Foundation Fieldbus10.6.2 Case Study-110.6.3 Case Study-210.6.4 Connecting Instruments10.6.5 Process of Segment Termination10.6.6 Advantages of Fieldbus Communication10.7 Comparison between Foundation Fieldbus H1 vs. PROFIBUS PA10.8 Structure of DCS10.8.1 Functional Input10.8.2 Supervisory Control and Data Acquisition10.9 ESD/DCS Interfaces10.10 Real-Time Optimization (RTO) Technology10.10.1 Real-Time Optimizer of the Typical Cycle10.10.2 Typical Functions of a Real-Time Optimizer (RTO)10.10.3 RTO Optimization Finds an Optimum at Each Iteration10.11 Elements of Dependability10.12 Threats Perception10.12.1 Measure of Detection10.13 Dependability of a DCS10.14 ConclusionBibliography
Chapter 11 Energizing the Future of Digital Technology in the Oil and Gas Industry
11.1 Industry Trends11.2 Scope of Expansion of Digital Technology in the Oil and Gas Industry11.3 Global Gas Markets11.3.1 Gas Markets in India’s Context11.3.2 Key Enablers to Evolve the Indian Gas Business11.3.3 Global Natural Gas Demand11.4 The Way Forward in the Gas Business11.5 Energy Perspective11.6 Initiatives in the Exploration and Production Sector by the Government of India11.6.1 Future Outlook of India’s E&P Industry11.7 Technological Way Forward11.7.1 Real-Time Virtual Flow Metering (RTVFM)11.7.2 New-Generation Production Logging11.7.3 High-Fidelity Seismic Imaging for Reliable Mapping of Complex Reservoirs11.7.4 3D Virtual Modeling and Drone Technology11.7.5 Machine Learning Based on the Early-Warning System11.7.6 Establishment of an Early-Warning Indicator System11.7.7 New Oil and Gas Well Hydro-Fracturing Technology11.7.8 Reservoir Simulation Modeling11.7.9 Transformation of Offshore Analog Fields to the Digital Domain by AI11.8 Digital Application in the Oil Refinery System11.9 Midstream Management11.10 Pipeline, Trading, and Procurement11.10.1 Fueling Efficiency in Upstream11.10.2 Future HR Digital Consumer11.11 Electric Vehicles11.12 Digital Power-Driven Consumer-Centric Marketing11.13 Downstream Applications11.14 Final Link in the Digital Chain11.15 Value Consolidation11.15.1 Rethink, Redefine, and Redesign Process11.15.2 Make Change Stick11.15.3 Rethink, Redefine, and Redesign11.16 Programming LanguagesBibliography
Index

Overview

The book essentially covers the growing role of AI in oil and gas industry, including digital technology in exploration phase to customer sales service along with a cloud-based digital storage of reservoir simulation data for modeling. It initiates with the description of AI system and its role towards oil and gas business.

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Machine Learning and Data Science in the Oil and Gas Industry

Publisher Resources

ISBN: 9781000857542

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills