Data Science and Machine Learning Applications in Subsurface Engineering

This book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems. Processing voluminous and complex data sets are the primary focus of the field of machine learning (ML). ML aims to develop data-driven methods and computational algorithms that can learn to identify complex and non-linear patterns to understand and predict the relationships between variables by analysing extensive data. Although ML models provide the final output for predictions, several steps need to be performed to achieve accurate predictions. These steps, data pre-processing, feature selection, feature engineering and outlier removal, are all contained in this book. New models are also developed using existing ML architecture and learning theories to improve the performance of traditional ML models and handle small and big data without manual adjustments. This research-oriented book will help subsurface engineers, geophysicists, and geoscientists become familiar with data science and ML advances relevant to subsurface engineering. Additionally, it demonstrates the use of data-driven approaches for salt identification, seismic interpretation, estimating enhanced oil recovery factor, predicting pore fluid types, petrophysical property prediction, estimating pressure drop in pipelines, bubble point pressure prediction, enhancing drilling mud loss, smart well completion and synthetic well log predictions.

Table of Contents:
Foreword Preface 1. Introduction 2. Enhancing Drilling Fluid Lost-circulation Prediction: Using Model Agnostic and Supervised Machine Learning Introduction Background of Machine Learning Regression Models Data Collection and Description Methodology Results and Discussion Conclusions References 3. Application of a Novel Stacked Ensemble Model in Predicting Total Porosity and Free Fluid Index via Wireline and NMR Logs Introduction Nuclear Magnetic Resonance Methodology Results and Discussion Conclusions References 4. Compressional and Shear Sonic Log Determination: Using Data-Driven Machine Learning Techniques Introduction Literature Review Background of Machine Learning Regression Models Data Collection and Description Methodology Results and Discussion Conclusions References 5. Data-Driven Virtual Flow Metering Systems Introduction VFM Key Characteristics Data Driven VFM Main Application Areas Methodology of Building Data-driven VFMs Field Experience with a Data-driven VFM System References 6. Data-driven and Machine Learning Approach in Estimating Multi-zonal ICV Water Injection Rates in a Smart Well Completion Introduction Brief Overview of Intelligent Well Completion Methodology Results and Discussion Conclusions References 7. Carbon Dioxide Low Salinity Water Alternating Gas (CO2 LSWAG) Oil Recovery Factor Prediction in Carbonate Reservoir: Using Supervised Machine Learning Models Introduction Methodology Results and Discussion Conclusion References 8. Improving Seismic Salt Mapping through Transfer Learning Using a Pre-trained Deep Convolutional Neural Network: A Case Study on Groningen Field Introduction Method Results and Discussion Conclusions References 9. Super-Vertical-Resolution Reconstruction of Seismic Volume Using a Pre-trained Deep Convolutional Neural Network: A Case Study on Opunake Field Introduction Brief Overview Methodology Results and Discussion Conclusions References 10. Petroleum Reservoir Characterisation: A Review from Empirical to Computer-Based Applications Introduction Empirical Models for Petrophysical Property Prediction Fractal Analysis in Reservoir Characterisation Application of Artificial Intelligence in Petrophysical Property Prediction Lithology and Facies Analysis Seismic Guided Petrophysical Property Prediction Hybrid Models of AI for Petrophysical Property Prediction Summary Challenges and Perspectives Conclusions References 11. Artificial Lift Design for Future Inflow and Outflow Performance for Jubilee Oilfield: Using Historical Production Data and Artificial Neural Network Models Introduction Methodology Results and Discussion Conclusions References 12. Modelling Two-phase Flow Parameters Utilizing Machine-learning Methodology Introduction Data Sources and Existing Correlations Methodology Results and Discussions Comparison between ML Algorithms and Existing Correlations Conclusions and Recommendations Nomenclature References Index