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Data Science and Machine Learning Applications in Subsurface Engineering

Otchere, Daniel Asante
Innbundet / 2024 / Engelsk

Produktdetaljer

ISBN
9781032433646
Publisert
2024-02-06
Utgiver
Vendor
CRC Press
Vekt
752 gr
Høyde
234 mm
Bredde
156 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Innbundet
Antall sider
306

Redaktør
Otchere, Daniel Asante

Om bidragsyterne

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.

Data Science and Machine Learning Applications in Subsurface Engineering

Otchere, Daniel Asante
Innbundet / 2024 / Engelsk
Otchere, Daniel Asante
Innbundet / 2024 / Engelsk
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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.
Les mer
This book provides comprehensive research and explores the different applications of data science and machine learning in subsurface engineering.
ForewordPreface1. Introduction 2. Enhancing Drilling Fluid Lost-circulation Prediction: Using Model Agnostic and Supervised Machine LearningIntroductionBackground of Machine Learning Regression ModelsData Collection and DescriptionMethodologyResults and DiscussionConclusionsReferences 3. Application of a Novel Stacked Ensemble Model in Predicting Total Porosity and Free Fluid Index via Wireline and NMR LogsIntroductionNuclear Magnetic ResonanceMethodologyResults and DiscussionConclusionsReferences 4. Compressional and Shear Sonic Log Determination: Using Data-Driven Machine Learning TechniquesIntroductionLiterature ReviewBackground of Machine Learning Regression ModelsData Collection and DescriptionMethodologyResults and DiscussionConclusionsReferences 5. Data-Driven Virtual Flow Metering SystemsIntroductionVFM Key CharacteristicsData Driven VFM Main Application AreasMethodology of Building Data-driven VFMsField Experience with a Data-driven VFM SystemReferences 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 CompletionMethodologyResults and DiscussionConclusionsReferences 7. Carbon Dioxide Low Salinity Water Alternating Gas (CO2 LSWAG) Oil Recovery Factor Prediction in Carbonate Reservoir: Using Supervised Machine Learning ModelsIntroductionMethodologyResults and DiscussionConclusionReferences 8. Improving Seismic Salt Mapping through Transfer Learning Using a Pre-trained Deep Convolutional Neural Network: A Case Study on Groningen FieldIntroductionMethodResults and DiscussionConclusionsReferences 9. Super-Vertical-Resolution Reconstruction of Seismic Volume Using a Pre-trained Deep Convolutional Neural Network: A Case Study on Opunake FieldIntroductionBrief OverviewMethodologyResults and DiscussionConclusionsReferences 10. Petroleum Reservoir Characterisation: A Review from Empirical to Computer-Based ApplicationsIntroductionEmpirical Models for Petrophysical Property PredictionFractal Analysis in Reservoir CharacterisationApplication of Artificial Intelligence in Petrophysical Property PredictionLithology and Facies AnalysisSeismic Guided Petrophysical Property PredictionHybrid Models of AI for Petrophysical Property PredictionSummaryChallenges and PerspectivesConclusionsReferences 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 References12. 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 ReferencesIndex
Les mer

Relaterte produkter

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.
Les mer
This book provides comprehensive research and explores the different applications of data science and machine learning in subsurface engineering.
ForewordPreface1. Introduction 2. Enhancing Drilling Fluid Lost-circulation Prediction: Using Model Agnostic and Supervised Machine LearningIntroductionBackground of Machine Learning Regression ModelsData Collection and DescriptionMethodologyResults and DiscussionConclusionsReferences 3. Application of a Novel Stacked Ensemble Model in Predicting Total Porosity and Free Fluid Index via Wireline and NMR LogsIntroductionNuclear Magnetic ResonanceMethodologyResults and DiscussionConclusionsReferences 4. Compressional and Shear Sonic Log Determination: Using Data-Driven Machine Learning TechniquesIntroductionLiterature ReviewBackground of Machine Learning Regression ModelsData Collection and DescriptionMethodologyResults and DiscussionConclusionsReferences 5. Data-Driven Virtual Flow Metering SystemsIntroductionVFM Key CharacteristicsData Driven VFM Main Application AreasMethodology of Building Data-driven VFMsField Experience with a Data-driven VFM SystemReferences 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 CompletionMethodologyResults and DiscussionConclusionsReferences 7. Carbon Dioxide Low Salinity Water Alternating Gas (CO2 LSWAG) Oil Recovery Factor Prediction in Carbonate Reservoir: Using Supervised Machine Learning ModelsIntroductionMethodologyResults and DiscussionConclusionReferences 8. Improving Seismic Salt Mapping through Transfer Learning Using a Pre-trained Deep Convolutional Neural Network: A Case Study on Groningen FieldIntroductionMethodResults and DiscussionConclusionsReferences 9. Super-Vertical-Resolution Reconstruction of Seismic Volume Using a Pre-trained Deep Convolutional Neural Network: A Case Study on Opunake FieldIntroductionBrief OverviewMethodologyResults and DiscussionConclusionsReferences 10. Petroleum Reservoir Characterisation: A Review from Empirical to Computer-Based ApplicationsIntroductionEmpirical Models for Petrophysical Property PredictionFractal Analysis in Reservoir CharacterisationApplication of Artificial Intelligence in Petrophysical Property PredictionLithology and Facies AnalysisSeismic Guided Petrophysical Property PredictionHybrid Models of AI for Petrophysical Property PredictionSummaryChallenges and PerspectivesConclusionsReferences 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 References12. 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 ReferencesIndex
Les mer

Produktdetaljer

ISBN
9781032433646
Publisert
2024-02-06
Utgiver
Vendor
CRC Press
Vekt
752 gr
Høyde
234 mm
Bredde
156 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Innbundet
Antall sider
306

Redaktør
Otchere, Daniel Asante

Om bidragsyterne

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.

Relaterte produkter