Artificial Intelligence and Data Analytics for Energy Exploration and Production

Cover
Title Page
Copyright Page
Dedication Page
Contents
Foreword
Preface
Chapter 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
Chapter 2 Machine Learning and Human Computer Interface
2.1 Introduction
2.2 Visualization of Machine Learning
2.3 Interactive Machine Learning
Chapter 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)
Chapter 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 á-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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 10 Applications 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
Chapter 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
Chapter 12 Applications in Production Optimization, Well Completion and Stimulation
12.1 Introduction
12.2 Production Optimization
12.3 Stimulation
12.4 Well Completion
Chapter 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
Chapter 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
Chapter 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
Chapter 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
EULA