发帖
雷达卡
,Prasad RU,2025
The integration of Artificial Intelligence (AI) and Generative AI (GenAI) into the petroleum industry marks a transformative shift in how professionals approach exploration, production, and operational optimization. The challenges of managing vast, complex datasets, improving efficiencies, and enhancing decision-making are at the core of the modern energy landscape, and AI technologies offer unprecedented opportunities to address these challenges.
Practical Introduction to AI and GenAI in Petroleum Engineering: A No-Code AI Practical Handbook for Petroleum Professionals is designed to serve as a comprehensive, hands-on guide for those seeking to leverage AI and GenAI in petroleum engineering. In this book, we explore the practical applications of AI technologies in key areas such as reservoir management, predictive maintenance, drilling optimization, and more. It is our aim to provide professionals with the necessary tools, techniques, and knowledge to seamlessly incorporate AI into their daily work, driving efficiency, cost reduction, and innovation.
A unique aspect of this book is the dedicated chapters on no-coding practical exposures for AI techniques such as classification, regression, and clustering, where we provide readers with practical exercises using real-world petroleum datasets. This chapter is designed not only to introduce fundamental AI concepts but also to offer practical experience, enabling petroleum professionals to apply AI tools directly to data and challenges they encounter in their work. Whether you're developing predictive models, enhancing reservoir simulations, or creating intelligent systems for field optimization, the hands-on exercises will empower you to use AI and GenAI to solve real-world problems.
This book is tailored to both engineers and data scientists—those already in the petroleum industry and those looking to transition from other fields. With a strong focus on practical application, the content will guide readers through the foundational principles of AI, then provide the practical techniques necessary to implement these concepts in the context of petroleum engineering.
As AI continues to evolve, we believe that the future of petroleum engineering lies in embracing these technologies to innovate and improve processes. By equipping yourself with the knowledge and practical experience offered in this book, you will be well-positioned to harness the power of AI and GenAI to meet the challenges of an ever-changing energy sector.
We invite you to explore this exciting frontier and begin your journey towards mastering AI in petroleum engineering.
Alternative filename
lgrsnf/Prasad RU. Practical Introduction to AI and GenAI in Petroleum Engineering. A No-Code AI Practical Handbook for Petroleum Professionals_2025.pdf
Table of Contents
1. Introduction to AI and Generative AI in Petroleum Engineering
1.1 Overview of Artificial Intelligence (AI) in Engineering
1.2 The Role of Generative AI in Technology Innovation
1.3 The Evolution of AI in Petroleum Engineering
2. Fundamentals of AI and Machine Learning
2.1 Key Concepts in Artificial Intelligence
2.2 Introduction to Machine Learning Algorithms
2.3 Deep Learning and Neural Networks in Petroleum Applications
3. Data Acquisition and Processing for AI in Petroleum Engineering
3.1 Types of Data in Petroleum Engineering (Seismic, Geological, etc.)
3.2 Data Preprocessing and Cleaning Techniques
3.3 Data Integration and Analysis for AI Models
4. AI for Exploration and Reservoir Characterization
4.1 AI in Seismic Imaging and Interpretation
4.2 Predictive Modeling for Reservoir Characterization
4.3 Using AI to Enhance Geological and Geophysical Data Analysis
5. Generative AI in Reservoir Simulation
5.1 Overview of Reservoir Simulation Models
5.2 Generative AI Techniques for Reservoir Prediction
5.3 AI-driven Simulation for Enhanced Decision Making
6. AI for Drilling Optimization
6.1 Predictive Maintenance and Real-time Monitoring
6.2 AI in Drilling Performance Optimization
6.3 Automating Drilling Operations with Machine Learning
7. AI in Production and Well Management
7.1 AI-driven Well Performance Prediction
7.2 Optimizing Production through AI Models
7.3 Smart Wells and IoT Integration in Petroleum Systems
8. Generative AI in Design and Engineering of Oil & Gas Systems
8.1 Generative Design Principles in Oil & Gas Equipment
8.2 AI-assisted Engineering for Equipment Reliability
8.3 Enhancing System Efficiency through Generative Algorithms
9. AI for Reservoir Management and Enhanced Oil Recovery (EOR)
9.1 AI in Monitoring and Controlling EOR Techniques
9.2 Data-driven Decisions in EOR Optimization
9.3 AI Models for Predicting EOR Outcomes and Performance
10. AI in Environmental Monitoring and Safety
10.1 AI for Leak Detection and Environmental Protection
10.2 Predicting Environmental Impact using AI Models
10.3 AI for Safety Management and Risk Assessment
11. Integrating AI and Blockchain for Petroleum Supply Chains
11.1 AI in Petroleum Supply Chain Optimization
11.2 Blockchain for Data Transparency and Security
11.3 Enhancing Supply Chain Efficiency with AI and Blockchain
12. The Future of AI and Generative AI in Petroleum Engineering
12.1 Emerging AI Technologies in the Petroleum Industry
12.2 The Role of AI in Sustainability and Green Energy Transition
12.3 The Challenges and Opportunities of AI in Future Petroleum
13. Beginning Your First Practical AI hands-on for Petroleum Engineering
13.1 Understanding Petroleum Dataset
13.2 Applying Practical AI Techniques on Petroleum Engineering Data
13.3 Practical No-Code AI Classification Techniques for Petroleum Engineering Dataset
13.4 Practical No-Code AI regression in Petroleum Engineering for predicting outcomes
13.5 Practical No-Code AI Clustering in Petroleum Engineering for predicting outcomes
14. Real-world case study examples
14.1 Case Study 1: AI for Drilling Optimization – ExxonMobil
14.2 Case Study 2: AI for Reservoir Management and Enhanced Oil Recovery (EOR) - Equinor
14.3 Case Study 3: AI in Seismic Imaging and Interpretation – BP (British Petroleum)
14.4 Case Study 4: AI in Production and Well Management – Shell
14.5 Case Study 5: Deep Learning and Neural Networks in Petroleum Applications – Equinor
14.6 Case Study 6: AI for Exploration and Reservoir Characterization in Petroleum Engineering – BP
14.7 Case Study 7: AI in Environmental Monitoring and Safety in Petroleum Engineering – Shell
14.8 Case Study 8: Generative AI in Reservoir Simulation – Equinor
14.9 Case Study 9: Generative Design Principles in Oil & Gas Equipment – BAKER HUGHES
14.10 Case Study 10: Generative AI Techniques for Reservoir Prediction – Chevron
Practical Introduction to AI and GenAI in Petroleum Engineering_ A No-Code AI Pr.zip
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