Data Science and Machine Learning Applications in Subsurface Engineering

ISBN-13: 9781032433646 / Twarda / 2024 / 306 str.

Opis

This book provides comprehensive research and explores the different applications of data science and machine learning in subsurface engineering.

Spis treści

PREFACE. INTRODUCTION. ENHANCING DRILLING FLUID LOST-CIRCULATION PREDICTION USING MODEL AGNOSTIC AND SUPERVISED MACHINE LEARNING. APPLICATION OF A NOVEL STACKED ENSEMBLE MODEL IN PREDICTING TOTAL POROSITY AND FREE FLUID INDEX VIA WIRELINE AND NMR LOGS. COMPRESSIONAL AND SHEAR SONIC LOG DETERMINATION USING DATA-DRIVEN MACHINE LEARNING TECHNIQUES. DATA-DRIVEN VIRTUAL FLOW METERING SYSTEMS. DATA-DRIVEN AND MACHINE LEARNING APPROACH IN ESTIMATING MULTI-ZONAL ICV WATER INJECTION RATES IN A SMART WELL COMPLETION. CARBON DIOXIDE LOW SALINITY WATER ALTERNATING GAS (CO2 LSWAG) OIL RECOVERY FACTOR PREDICTION IN CARBONATE RESERVOIR USING SUPERVISED MACHINE LEARNING MODELS. IMPROVING SEISMIC SALT MAPPING THROUGH TRANSFER LEARNING USING A PRE-TRAINED DEEP CONVOLUTIONAL NEURAL NETWORK: A CASE STUDY ON GRONINGEN FIELD. SUPER-VERTICAL-RESOLUTION RECONSTRUCTION OF SEISMIC VOLUME USING A PRE-TRAINED DEEP CONVOLUTIONAL NEURAL NETWORK: A CASE STUDY ON OPUNAKE FIELD. PETROLEUM RESERVOIR CHARACTERISATION: A REVIEW FROM EMPIRICAL TO COMPUTER-BASED APPLICATIONS. ARTIFICIAL LIFT DESIGN FOR FUTURE INFLOW AND OUTFLOW PERFORMANCE FOR JUBILEE OILFIELD USING HISTORICAL PRODUCTION DATA AND ARTIFICIAL NEURAL NETWORK MODELS. MODELLING TWO-PHASE FLOW PARAMETERS UTILIZING DATA-DRIVEN MACHINE LEARNING METHODOLOGY.

O autorze

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.