Data Science and Machine Learning Applications in Subsurface Engineering
Language: English
Published by Taylor and Francis Ltd, GB, 2025
- Softcover
- New
Seller: Rarewaves.com USA, London, London, United KingdomRarewaves.com USA
AbeBooks seller since June 11, 2025
Condition: New
£ 89.30
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Add to basketItem description from seller
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.…
Seller Inventory # LU-9781032433653
- Title
- Data Science and Machine Learning Applications in Subsurface Engineering
- Author
- Daniel Asante Otchere
- Publisher
- Taylor and Francis Ltd, GB
- Publication year
- 2025
- Condition
- New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1032433655
- ISBN 13
- 9781032433653
- Item weight
- 460 grams
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.
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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