Artificial Intelligence and Data Analytics for Energy Exploration and Production

ARTIFICAL INTELLIGENCE AND DATA ANALYTICS FOR ENERGY EXPLORATION AND PRODUCTION This 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 in the energy industry, covering the most comprehensive and updated new processes, concepts, and practical applications in the field. 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.

Table of Contents:
Foreword xvii Preface xix 1 Introduction to Modern Intelligent Data Analysis 1 1.1 Introduction 1 1.2 Introduction to Machine Learning 4 1.3 General Example of Machine Learning 8 1.4 E&P Examples of Machine Learning 9 1.5 Objectives of the Book 10 1.6 Outline of Chapters 10 2 Machine Learning and Human Computer Interface 23 2.1 Introduction 23 2.2 Visualization of Machine Learning 24 2.3 Interactive Machine Learning 30 3 Artificial Neural Networks 39 3.1 Introduction 39 3.2 Structure of Biological Neurons 41 3.2.1 Artificial Neurons Structure 42 3.2.2 Integration Function 43 3.2.3 Activation Function 44 3.2.4 Decision Boundaries 46 3.3 Learning and Deep Learning Process for ANN 47 3.3.1 ANN Learning 47 3.3.2 Deep Learning 50 3.4 Different Structures of ANNs 51 3.4.1 Multi-Layer Perceptron (MLP) 53 3.4.2 Radial Basis Function Neural Networks (RBF) 54 3.4.3 Modular Neural Networks (Committee Machines) 55 3.4.4 Self-Organizing Networks 58 3.4.5 Kohonen Networks 61 3.4.6 Generalized Regression (GRNN) and Probabilistic (pnn) 62 3.4.7 Convolutional Neural Network (CNN) 64 3.4.8 Generative Adversarial Network (GAN) 65 3.4.9 Recurrent Neural Network (RNN) 66 3.4.10 Long/Short-Term Memory (LSTM) 67 3.5 Pre-Processing of the ANN Input Data 67 3.5.1 Dimensionality Reduction 69 3.5.2 Artificial Neural Networks (ANN) Versus Conventional Computing Tools (CCT) 70 3.6 Combining ANN with Human Intelligence 70 3.7 ANN Applications to the Exploration and Production (E&P) Problems 73 3.7.1 First Break Picking Seismic Arrivals 74 3.7.2 Porosity Prediction in a CO2 Injection Project 76 3.7.3 CNN for Permeability Prediction 78 3.7.4 Creating Pseudologs 81 3.7.5 Facies Classification with Exhustive PNN 81 3.7.6 Machine Learning for Estimating the Stimulated Reservoir Volume (SRV) 83 4 Fuzzy Logic 85 4.1 Introduction to Fuzzy Logic 85 4.2 Theoretical Foundation and Formal Treatment of Fuzzy Logic 90 4.2.1 Some Definitions in Fuzzy Logic 93 4.2.2 Fuzzy Propositions 94 4.2.3 Thresholding or α-Cut Concept 95 4.2.4 Additional Properties of Fuzzy Logic 96 4.2.5 Fuzzy Extensions of Classical Mathematics 98 4.2.5.1 Fuzzy Averaging 98 4.2.5.2 Fuzzy Arithmetic 99 4.2.5.3 Fuzzy Function and Fuzzy Patches 100 4.2.5.4 Fuzzy K-Means and C-Means or Clustering 103 4.2.5.5 Fuzzy Kriging 105 4.2.5.6 Fuzzy Differential Equations 108 4.2.6 Fuzzy Systems, Fuzzy Rules 109 4.2.6.1 Fuzzy Rules 110 4.2.6.2 Fuzzy Knowledge-Based Systems 112 4.2.7 Type-2 Fuzzy Sets and Systems 114 4.2.8 Computing with Words and Linguistic Variable 116 4.2.8.1 CWW versus Fuzzy Logic 116 4.2.8.2 Linguistic Variables 118 4.2.9 Mining Fuzzy Rules from Examples 120 4.2.10 Fuzzy Logic Software 121 4.3 Oil and Gas Industry Application Domain Discussion 122 4.3.1 Linguistic Goal-Oriented Decision Making (LGODM) to Optimize Enhanced Oil Recovery in the Steam Injection Process 123 4.3.2 Use of Fuzzy Clustering in Perforation Design 124 4.3.3 Stratigraphic Interpretation Using Fuzzy Rules 127 4.3.4 Fuzzy Logic-Based Interpolation to Improve Seismic Resolution 132 4.4 Conclusions 135 5 Integration of Conventional and Unconventional Methods 137 5.1 Strengths and Weaknesses of Different Computing Techniques 137 5.2 Why Integrate Different Methods? 140 5.2.1 Neuro-Fuzzy Methods 141 5.2.1.1 Why Combine NN and FL? 142 5.2.1.2 NN-Based FL Inference 143 5.2.2 Neuro-Genetic Methods 145 5.2.3 Fuzzy-Genetic (FG) 147 5.2.4 Soft Computing - Conventional (SC) Methods 148 5.3 Oil and Gas Applications of NF, NG, FG, CF, and CN 150 5.3.1 NN-CM- Rock Permeability Forecast Using Machine Learning and Monte Carlo Committee Machines 151 5.3.2 (NN-CM) Pseudo Density Log Generation Using Artificial Neural Network 154 5.3.2.1 Well Log Data Preprocessing 155 5.3.2.2 Well Log Data Mining 156 5.3.2.3 Data Postprocessing for Generating Pseudo Density Logs 157 5.3.3 NN-FL- Integrating Neural Networks and Fuzzy Logic for Improved Reservoir Property Prediction and Prospect Ranking 159 5.3.4 (FL-NN-CM) Gas Leak Detection 161 5.3.5 GA-FL for Improving Oil Recovery Factor 162 5.3.6 GA-FL to Improve Coal Mining Process 165 5.4 Conclusions 166 6 Natural Language Processing 167 6.1 Introduction 167 6.2 A Brief History of NLP 168 6.3 Basics of the NLP Method 171 6.3.1 Sentence Segmentation 171 6.3.2 Tokenization 172 6.3.3 Parts of Speech Prediction 172 6.3.4 Lemmatization 173 6.3.5 Stop Words Removal 173 6.3.6 Dependency Parsing 174 6.3.7 Named Entity Recognition 175 6.3.8 Coreference Resolution 175 6.4 Use Cases of NLP 175 6.5 Applications of NLP in the Oil and Gas Industry 177 6.6 Conclusion 193 7 Data Science and Big Data Analytics 195 7.1 Introduction 195 7.2 Big Data 195 7.3 Algorithms and Models in Data Sciences 197 7.3.1 Automated Machine Learning 198 7.3.2 Interpretable, Explainable, and Privacy-Preserving Machine Learning 198 7.4 Infrastructure and Tooling for Data Science 202 7.5 Oil and Gas Focused Issues Associated with Data Science and Big Data High Performance Computing in the Age of Big Data 206 7.5.1 Big Data in Oil and Gas 208 7.5.2 High-Performance Computing for Handling Big Data in Subsurface Imaging 209 7.5.3 Access to Oil and Gas Data 210 8 Applications of Machine Learning in Exploration 213 8.1 Introduction 213 8.1.1 Petroleum System and Exploration Risk Factors 214 8.1.2 Data Acquisition, Processing, and Integration for Exploration 215 8.1.3 Exploration and Appraisal Drilling 217 8.2 AI for Exploration Risk Assessment 218 8.2.1 Petroleum System Risk Assessment 218 8.2.2 Geological Risk Assessment Level of Knowledge and Experience (LoK) 221 8.3 AI for Data Acquisition, Processing, and Integration in Exploration 224 8.3.1 Auto-Picking for Micro-Seismic Data 224 8.3.2 Facies Classification Using Supervised CNN and Semi-Supervised GAN 226 8.3.3 Generating Gas Chimney Cube Using MLP ANN 227 8.3.4 Reservoir Geostatistical Estimation of Imprecise Information Using Fuzzy Kriging Approach 230 8.3.5 Fracture Zone Identification Using Seismic, Micro-Seismic and Well Log Data 232 9 Applications in Oil and Gas Drilling 239 9.1 Real-Time Measurements in Drilling Automation 239 9.2 Event Detection in Drilling 243 9.3 Rate of Penetration Estimations 251 9.4 Estimation of the Bottom Hole and Formation Temperature by Drilling Data 255 9.5 Drilling Dysfunctions 258 9.6 Machine Learning Applications in Well Drilling Operations 262 9.7 Conclusion 269 10 Applications in Reservoir Characterization and Field Development Optimization 271 10.1 Introduction 271 10.1.1 Reservoir Characterization 273 10.1.1.1 Porous Media Characterization 275 10.1.1.2 Porosity 278 10.1.1.3 Permeability 278 10.1.1.4 Permeability-Porosity Relationship 281 10.1.2 Machine Learning Applications for Reservoir Characterization 282 10.1.2.1 Reservoir Modeling 291 10.1.2.2 Capabilities of Data Mining 293 10.1.2.3 Computational Intelligence in Petroleum Application 294 10.1.2.4 Computational Intelligence in Permeability and Porosity Prediction 295 10.1.2.5 Hybrid Computational Intelligence (HCI) 296 10.1.2.6 Ensemble Machine Learning for Reservoir Characterization 297 10.1.2.7 Prediction of Sand Fraction (SF) by Using Machine Learning 300 10.1.2.8 Machine Learning Application in Classification of Water Saturation 301 10.1.2.9 Physics-Informed Machine Learning for Real-Time Reservoir Management 302 10.1.2.10 Well-Log and Seismic Data Integration for Reservoir Characterization 303 10.1.2.11 Machine Learning for Homogeneous Reservoir Characterization 304 10.1.2.12 The Gradient Boosting Method for Reservoir Characterization 305 10.1.2.13 The Parameterizing Uncertainty for Reservoir Characterization 306 10.1.2.14 Geochemistry and Chemostratigraphy for Reservoir Characterization 307 10.2 Conclusions 310 11 Machine Learning Applications in Production Forecasting 313 11.1 Introduction 313 11.2 Analytical Solution 315 11.2.1 Type Curves 316 11.2.2 Limitations 317 11.3 Numerical Solution 317 11.3.1 Limitations 318 11.3.2 Machine Learning Applications 319 11.4 Decline Curve Analysis (DCA) 320 11.4.1 Arps Method 320 11.4.2 Method Modifications of the Arps Method 321 11.4.3 Limitations 326 11.4.4 Machine Learning Applications 327 11.5 Data-Driven Solutions 330 11.5.1 Sensitivity Analysis 331 11.5.2 Machine Learning Applications 331 11.5.3 Limitations 349 11.6 Conclusion 350 12 Applications in Production Optimization, Well Completion and Stimulation 353 12.1 Introduction 353 12.2 Production Optimization 354 12.3 Stimulation 358 12.4 Well Completion 363 13 Machine Learning Applications in Reservoir Engineering and Reservoir Simulation 369 13.1 Introduction 369 13.2 Fluid Properties Estimation with Machine Learning Methods 370 13.2.1 Machine Learning Applications in Reservoir Simulation 376 13.2.2 Machine Learning Applications in Geothermal Reservoir Engineering 390 13.3 Machine Learning Applications in Well Testing 397 13.4 Conclusion 403 14 Machine Learning Applications in Artificial Lift 405 14.1 Introduction 405 14.2 Big Data and Analytical Solutions in Drilling Operations 407 14.3 Machine Learning 408 14.3.1 Using Machine Learning in the Oil and Gas Industry 410 14.3.2 Failure Prediction Frameworks and Algorithms for Artificial Lift Systems 413 14.4 Artificial Lift 415 14.4.1 Brief Overview of Production Systems Analysis 416 14.4.2 Types of the Artificial Lift Systems 418 14.4.2.1 Plunger Lift 418 14.4.2.2 Gas Lift-Continuous and Intermittent 419 14.4.2.3 Pumps 421 14.4.3 Artificial Lift Applications, Monitoring, and Automation Services 424 14.5 Conclusion 429 15 Machine Learning Applications in Enhanced Oil Recovery (EOR) 431 15.1 Introduction 431 15.2 Enhanced Oil Recovery 432 15.2.1 Thermal Methods 437 15.2.2 Chemical Methods 439 15.2.3 Gas Methods 441 15.2.4 Microbial Methods 444 15.3 Enhanced Oil Recovery (EOR) Reservoirs 445 15.4 The Economic Value of EOR 453 15.5 Simulation Models 454 15.6 Machine Learning (ML) 455 15.7 Machine Learning in Enhanced Oil Recovery (EOR) Applications 458 15.8 Machine Learning in Enhanced Oil Recovery (EOR) Screening 462 15.9 Applications 465 15.10 Software 467 15.11 Conclusion 468 16 Conclusions and Future Directions 471 16.1 Technology Advances in Artificial Intelligence and Data Science 471 16.1.1 Technology Advances in Artificial Intelligence and Data Science 471 16.1.2 Future Directions of Machine Learning and Human-Computer Interface 471 16.1.3 Future Directions of Artificial Neural Networks 474 16.1.4 Future Directions of Fuzzy Logic 476 16.1.5 Future Directions of Integrated AI Techniques 477 16.1.6 Future Directions of Natural Language Processing 479 16.1.7 Future Directions of Data Science and Big Data Analytics 480 16.2 Future Trends in the Energy Applications of Artificial Intelligence and Data Science 482 16.2.1 Future Trends in Exploration Applications of AI-DA 483 16.2.2 Future Trends in Drilling Applications of AI-DA 484 16.2.3 Future Trends in Reservoir Characterization Applications of AI-DA 486 16.2.4 Future Trends in Production Forecasting Applications of AI-DA 487 16.2.5 Future Trends in Production Optimization, Well Completion and Stimulation Applications of AI-DA 488 16.2.6 Future Trends in Reservoir Engineering and Simulation Applications of AI-DA 489 16.2.7 Future Trends in Artificial Lift Applications of AI-DA 491 16.2.8 Future Work for Machine Learning Applications in EOR 492 References 495 Index 555