This book provides comprehensive research and explores the different applications of data science and machine learning in subsurface engineering.
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Daniel Asante Otchere is an AI/ML Scientific Engineer at the Institute of Computational and Data Sciences (ICDS) at Pennsylvania State University, USA. He holds a PhD in petroleum engineering from Universiti Teknologi PETRONAS (UTP) in Malaysia, a Master's degree in Petroleum Geoscience from the University of Manchester in UK, and a Bachelor's degree in Geological Engineering from the University of Mines and Technology in Ghana. Professionally, Daniel has extensive experience across the mining and oil and gas industry, working on several onshore and offshore projects that have had a significant impact on the industry in Africa and South East Asia. He serves as a technical committee member of the World Geothermal Congress and teaches several AI topics on his YouTube channel "Study with Dani". His expertise has resulted in numerous collaborative research efforts, yielding several articles published in renowned journals and conferences. He was recognised for excellence in teaching and research in the Petroleum Engineering Department at UTP and received the 2021 best postgraduate student and the Graduate Assistant merit award in 2021 and 2022. He enjoys watching movies, listening to Highlife and Afrobeats music, hockey, and playing football. He also excels in the realm of video games, having won numerous PlayStation-FIFA tournaments held in the United Kingdom, Ghana, and Malaysia.
Inhaltsangabe
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
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
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