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Artificial Intelligence and Data Analytics for Exploration and Production

By Fred Aminzadeh, Cenk Temizel, and Yasin Hajizadeh
Copyright: 2022   |   Status: Published
ISBN: 9781119879695  |  Hardcover  |  
605 pages
Price: $249 USD
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One Line Description
The groundbreaking new book is written by some of the foremost authorities on the application of data science and artificial intelligence techniques in exploration and production industry. This volume presents the most comprehensive and updated new processes, concepts, and practical applications in the field.


Audience
Professionals from many disciplines such as petroleum engineers, geophysicists, geologists, chemical engineers, environmental engineers, as well as those with AI and data science interest would benefit from this book. In addition, students and professors of petroleum and geosciences disciplines, cloud solution architects, applied machine learning researchers would find the book a valuable resource.


Description
The book provides an in-depth treatment of the foundations of Artificial Intelligence (AI) Machine Learning, and Data Analytics (DA). It also includes many of AI-DA applications in oil and gas reservoirs exploration, development, and production. The book covers the basic technical details on many tools used in "smart oil fields". This includes topics such as pattern recognition, neural networks, fuzzy logic, evolutionary computing, expert systems, artificial intelligence machine learning, human-computer interface, natural language processing, data analytics and next generation visualization. While theoretical details will be kept to the minimum, these topics are introduced from oil and gas applications viewpoints.

In this volume, many case histories from the recent applications of intelligent data to a number of different oil and gas problems are highlighted. The applications cover a wide spectrum of practical problems from exploration to drilling and field development to production optimization, artificial lift, and secondary recovery. Also, the authors demonstrate the effectiveness of intelligent data analysis methods in dealing with many oil and gas problems requiring combining machine and human intelligence as well as dealing with linguistic and imprecise data and rules.


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Author / Editor Details
Fred Aminzadeh, PhD, is an expert in artificial intelligence and energy. He has been a professor at the University of Houston and University of Southern California. He worked at dGB,Unocal (now part of Chevron) and Bell Laboratories. His work experience includes fossil energy, geothermal energy, and carbon sequestration. He served as the president of the Society of Exploration Geophysicists. He has authored over 15 books and holds several patents. He was the editor in chief of the Journal of Sustainable Energy Engineering. Currently, he is president of FACT, an energy services company. He is also a member of the technical advisory board of DOE/NETL’s SMART initiative and an adjunct professor at the University of North Dakota.

Cenk Temizel is a senior reservoir engineer with Saudi Aramco. He has over 15 years of experience in reservoir simulation, data analytics, smart fields, unconventional, and enhanced oil recovery with Aera Energy, Schlumberger, and Halliburton in the Middle East, the US, and the UK. He is the recipient of the Aramco Unconventional Resources Technical Contribution Award (2020), 2nd place at SPE Global R&D Competition at ATCE 2014 in Amsterdam, and the Halliburton Applause Award in Innovation (2012). He has published numerous papers in reservoir engineering, enhanced oil recovery processes, machine learning, and smart fields and holds several patents in these areas.

Yasin Hajizadeh is the CTO of Maillance, a startup focusing on democratizing machine learning for petroleum engineers. He is also the founder and CEO of Nowos, a consulting firm on digital transformation. Previously, Yasin was a program manager of Azure ML and IoT at Microsoft. He also worked for Schlumberger as a data scientist and reservoir engineer, and at CMG as an optimization and uncertainty quantification scientist. Yasin holds a PhD in petroleum engineering from Heriot Watt University and a Masters in technology management from Memorial University of Newfoundland.

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Table of Contents
Foreword
Preface
1. Introduction to Modern Intelligent Data Analysis
1.1 Introduction
1.2 Introduction to Machine Learning
1.3 General Example of Machine Learning
1.4 E&P Examples of Machine Learning
1.5 Objectives of the Book
1.6 Outline of Chapters
2. Machine Learning and Human Computer Interface
2.1 Introduction
2.2 Visualization of Machine Learning
2.3 Interactive Machine Learning
3. Artificial Neural Networks
3.1 Introduction
3.2 Structure of Biological Neurons
3.2.1 Artificial Neurons Structure
3.2.2 Integration Function
3.2.3 Activation Function
3.2.4 Decision Boundaries
3.3 Learning and Deep Learning Process for ANN
3.3.1 ANN Learning
3.3.2 Deep Learning
3.4 Different Structures of ANNs
3.4.1 Multi-Layer Perceptron (MLP)
3.4.2 Radial Basis Function Neural Networks (RBF)
3.4.3 Modular Neural Networks (Committee Machines)
3.4.4 Self-Organizing Networks
3.4.5 Kohonen Networks
3.4.6 Generalized Regression (GRNN) and Probabilistic (PNN)
3.4.7 Convolutional Neural Network (CNN)
3.4.8 Generative Adversarial Network (GAN)
3.4.9 Recurrent Neural Network (RNN)
3.4.10 Long/Short-Term Memory (LSTM)
3.5 Pre-Processing of the ANN Input Data
3.5.1 Dimensionality Reduction
3.5.2 Artificial Neural Networks (ANN) Versus Conventional Computing Tools (CCT)
3.6 Combining ANN with Human Intelligence
3.7 ANN Applications to the Exploration and Production (E&P) Problems
3.7.1 First Break Picking Seismic Arrivals
3.7.2 Porosity Prediction in a CO2 Injection Project
3.7.3 CNN for Permeability Prediction
3.7.4 Creating Pseudologs
3.7.5 Facies Classification with Exhustive PNN
3.7.6 Machine Learning for Estimating the Stimulated Reservoir Volume (SRV)
4 Fuzzy Logic
4.1 Introduction to Fuzzy Logic
4.2 Theoretical Foundation and Formal Treatment of Fuzzy Logic
4.2.1 Some Definitions in Fuzzy Logic
4.2.2 Fuzzy Propositions
4.2.3 Thresholding or α-Cut Concept
4.2.4 Additional Properties of Fuzzy Logic
4.2.5 Fuzzy Extensions of Classical Mathematics
4.2.5.1 Fuzzy Averaging
4.2.5.2 Fuzzy Arithmetic
4.2.5.3 Fuzzy Function and Fuzzy Patches
4.2.5.4 Fuzzy K-Means and C-Means or Clustering
4.2.5.5 Fuzzy Kriging
4.2.5.6 Fuzzy Differential Equations
4.2.6 Fuzzy Systems, Fuzzy Rules
4.2.6.1 Fuzzy Rules
4.2.6.2 Fuzzy Knowledge-Based Systems
4.2.7 Type-2 Fuzzy Sets and Systems
4.2.8 Computing with Words and Linguistic Variable
4.2.8.1 CWW versus Fuzzy Logic
4.2.8.2 Linguistic Variables
4.2.9 Mining Fuzzy Rules from Examples
4.2.10 Fuzzy Logic Software
4.3 Oil and Gas Industry Application Domain Discussion
4.3.1 Linguistic Goal-Oriented Decision Making (LGODM) to Optimize Enhanced Oil Recovery in the Steam Injection Process
4.3.2 Use of Fuzzy Clustering in Perforation Design
4.3.3 Stratigraphic Interpretation Using Fuzzy Rules
4.3.4 Fuzzy Logic-Based Interpolation to Improve Seismic Resolution
4.4 Conclusions
5. Integration of Conventional and Unconventional Methods
5.1 Strengths and Weaknesses of Different Computing Techniques
5.2 Why Integrate Different Methods?
5.2.1 Neuro-Fuzzy Methods
5.2.1.1 Why Combine NN and FL?
5.2.1.2 NN-Based FL Inference
5.2.2 Neuro-Genetic Methods
5.2.3 Fuzzy-Genetic (FG)
5.2.4 Soft Computing - Conventional (SC) Methods
5.3 Oil and Gas Applications of NF, NG, FG, CF, and CN
5.3.1 NN-CM- Rock Permeability Forecast Using Machine Learning and Monte Carlo Committee Machines
5.3.2 (NN-CM) Pseudo Density Log Generation Using Artificial Neural Network
5.3.2.1 Well Log Data Preprocessing
5.3.2.2 Well Log Data Mining
5.3.2.3 Data Postprocessing for Generating Pseudo Density Logs
5.3.3 NN-FL- Integrating Neural Networks and Fuzzy Logic for Improved Reservoir Property Prediction and Prospect Ranking
5.3.4 (FL-NN-CM) Gas Leak Detection
5.3.5 GA-FL for Improving Oil Recovery Factor
5.3.6 GA-FL to Improve Coal Mining Process
5.4 Conclusions
6. Natural Language Processing
6.1 Introduction
6.2 A Brief History of NLP
6.3 Basics of the NLP Method
6.3.1 Sentence Segmentation
6.3.2 Tokenization
6.3.3 Parts of Speech Prediction
6.3.4 Lemmatization
6.3.5 Stop Words Removal
6.3.6 Dependency Parsing
6.3.7 Named Entity Recognition
6.3.8 Coreference Resolution
6.4 Use Cases of NLP
6.5 Applications of NLP in the Oil and Gas Industry
6.6 Conclusion
7. Data Science and Big Data Analytics
7.1 Introduction
7.2 Big Data
7.3 Algorithms and Models in Data Sciences
7.3.1 Automated Machine Learning
7.3.2 Interpretable, Explainable, and Privacy-Preserving Machine Learning
7.4 Infrastructure and Tooling for Data Science
7.5 Oil and Gas Focused Issues Associated with Data Science and Big Data High Performance Computing in the Age of Big Data
7.5.1 Big Data in Oil and Gas
7.5.2 High-Performance Computing for Handling Big Data in Subsurface Imaging 7.5.3 Access to Oil and Gas Data
8. Applications of Machine Learning in Exploration
8.1 Introduction
8.1.1 Petroleum System and Exploration Risk Factors
8.1.2 Data Acquisition, Processing, and Integration for Exploration
8.1.3 Exploration and Appraisal Drilling
8.2 AI for Exploration Risk Assessment
8.2.1 Petroleum System Risk Assessment
8.2.2 Geological Risk Assessment Level of Knowledge and Experience (LoK)
8.3 AI for Data Acquisition, Processing, and Integration in Exploration
8.3.1 Auto-Picking for Micro-Seismic Data
8.3.2 Facies Classification Using Supervised CNN and Semi-Supervised GAN
8.3.3 Generating Gas Chimney Cube Using MLP ANN
8.3.4 Reservoir Geostatistical Estimation of Imprecise Information Using Fuzzy Kriging Approach
8.3.5 Fracture Zone Identification Using Seismic, Micro-Seismic and Well Log Data
9. Applications in Oil and Gas Drilling
9.1 Real-Time Measurements in Drilling Automation
9.2 Event Detection in Drilling
9.3 Rate of Penetration Estimations
9.4 Estimation of the Bottom Hole and Formation Temperature by Drilling Data
9.5 Drilling Dysfunctions
9.6 Machine Learning Applications in Well Drilling Operations
9.7 Conclusion
10. Application of Machine Learning in Reservoir Characterization and Field Development Optimization
10.1 Introduction
10.1.1 Reservoir Characterization
10.1.1.1 Porous Media Characterization
10.1.1.2 Porosity
10.1.1.3 Permeability
10.1.1.4 Permeability-Porosity Relationship
10.1.2 Machine Learning Applications for Reservoir Characterization
10.1.2.1 Reservoir Modeling
10.1.2.2 Capabilities of Data Mining
10.1.2.3 Computational Intelligence in Petroleum Application
10.1.2.4 Computational Intelligence in Permeability and Porosity Prediction
10.1.2.5 Hybrid Computational Intelligence (HCI)
10.1.2.6 Ensemble Machine Learning for Reservoir Characterization
10.1.2.7 Prediction of Sand Fraction (SF) by Using Machine Learning
10.1.2.8 Machine Learning Application in Classification of Water Saturation
10.1.2.9 Physics-Informed Machine Learning for Real-time Reservoir Management 10.1.2.10 Well-Log and Seismic Data Integration for Reservoir Characterization
10.1.2.11 Machine Learning for Homogeneous Reservoir Characterization
10.1.2.12 The Gradient Boosting Method for Reservoir Characterization
10.1.2.13 The Parameterizing Uncertainty for Reservoir Characterization
10.1.2.14 Geochemistry and Chemostratigraphy for Reservoir Characterization
10.2 Conclusions
11. Machine Learning Applications in Production Forecasting
11.1 Introduction
11.2 Analytical Solution
11.2.1 Type Curves
11.2.2 Limitations
11.3 Numerical Solution
11.3.1 Limitations
11.3.2 Machine Learning Applications
11.4 Decline Curve Analysis (DCA)
11.4.1 Arps Method
11.4.2 Method Modifications of the Arps Method
11.4.3 Limitations
11.4.4 Machine Learning Applications
11.5 Data-Driven Solutions
11.5.1 Sensitivity Analysis
11.5.2 Machine Learning Applications
11.5.3 Limitations
11.6 Conclusion
12. Applications in Production Optimization, Well Completion and Stimulation
12.1 Introduction
12.2 Production Optimization
12.3 Stimulation
12.4 Well Completion
13. Machine Learning Applications in Reservoir Engineering and Reservoir Simulation
13.1 Introduction
13.2 Fluid Properties Estimation with Machine Learning Methods
13.2.1 Machine Learning Applications in Reservoir Simulation
13.2.2 Machine Learning Applications in Geothermal Reservoir Engineering
13.3 Machine Learning Applications in Well Testing
13.4 Conclusion
14. Machine Learning Applications in Artificial Lift
14.1 Introduction
14.2 Big Data and Analytical Solutions in Drilling Operations
14.3 Machine Learning
14.3.1 Using Machine Learning in the Oil and Gas Industry
14.3.2 Failure Prediction Frameworks and Algorithms for Artificial Lift Systems
14.4 Artificial Lift
14.4.1 Brief Overview of Production Systems Analysis
14.4.2 Types of the Artificial Lift Systems
14.4.2.1 Plunger Lift
14.4.2.2 Gas Lift-Continuous and Intermittent
14.4.2.3 Pumps
14.4.3 Artificial Lift Applications, Monitoring, and Automation Services
14.5 Conclusion
15. Machine Learning Applications in Enhanced Oil Recovery (EOR)
15.1 Introduction
15.2 Enhanced Oil Recovery
15.2.1 Thermal Methods
15.2.2 Chemical Methods
15.2.3 Gas Methods
15.2.4 Microbial Methods
15.3 Enhanced Oil Recovery (EOR) Reservoirs
15.4 The Economic Value of EOR
15.5 Simulation Models
15.6 Machine Learning (ML)
15.7 Machine Learning in Enhanced Oil Recovery (EOR) Applications
15.8 Machine Learning in Enhanced Oil Recovery (EOR) Screening
15.9 Applications
15.10 Software
15.11 Conclusion
16. Conclusions and Future Directions
16.1 Technology Advances in Artificial Intelligence and Data Science
16.1.1 Technology Advances in Artificial Intelligence and Data Science
16.1.2 Future Directions of Machine Learning and Human-Computer Interface
16.1.3 Future Directions of Artificial Neural Networks
16.1.4 Future Directions of Fuzzy Logic
16.1.5 Future Directions of Integrated AI Techniques
16.1.6 Future Directions of Natural Language Processing
16.1.7 Future Directions of Data Science and Big Data Analytics
16.2 Future Trends in the Energy Applications of Artificial Intelligence and Data Science
16.2.1 Future Trends in Exploration Applications of AI-DA
16.2.2 Future Trends in Drilling Applications of AI-DA
16.2.3 Future Trends in Reservoir Characterization Applications of AI-DA
16.2.4 Future Trends in Production Forecasting Applications of AI-DA
16.2.5 Future Trends in Production Optimization, Well Completion and Stimulation Applications of AI-DA
16.2.6 Future Trends in Reservoir Engineering and Simulation Applications of AI-DA
16.2.7 Future Trends in Artificial Lift Applications of AI-DA
16.2.8 Future Work for Machine Learning Applications in EOR
References
Index

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