[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121622-en":3,"doc-seo-121622-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121622,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Reservoir Description via Statistical and Machine-Learning Approaches - Dissertation","Reservoir description supports decision-making in hydrocarbon development by linking geophysical interpretation with reservoir modeling. The dissertation addresses uncertainty estimation through stochastic reservoir modeling conditioned on fluid production history, improving risk and profit management during exploration and production. It focuses on well-log interpretation for high-resolution estimation of subsurface rock properties, while highlighting limitations of conventional petrophysical models for complex permeability relationships. Data-driven inferential methods, including machine learning, are used for improved permeability prediction in spatially complex rocks.","Copyright by Wen Pan  \n2022  \nThe Dissertation Committee for Wen Pan Certifies that this is the approved version  \nof the following dissertation:  \nReservoir Description via Statistical and Machine-Learning Approaches  \nCommittee:  \n\n| Carlos Torres-Verdín, Supervisor |\n| --- |\n| Ian J. Duncan, Co-Supervisor |\n| Michael J. Pyrcz, Co-Supervisor |\n| Zoya Heidari |\n\nLarry W. Lake  \nReservoir Description via Statistical and Machine-Learning Approaches  \nby  \nWen Pan  \nDissertation  \nPresented to the Faculty of the Graduate School of  \nThe University of Texas at Austin  \nin Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nDoctor of Philosophy  \nThe University of Texas at Austin  \nAugust 2022  \nDedication  \nTo my family.  \nAcknowledgements  \nI would like to thank all people who supported me during the completion of this dissertation. I am extremely grateful that I could have this opportunity to work with my advisor, Dr. Carlos Torres-Verdín, and co-advisors, Dr. Ian Duncan and Dr. Michael Pyrcz. [Throughout my Ph.D. study](Throughout my Ph.D. study), they had been consistently giving me enormous guidance and support that I would never have expected. Not only did I deepen my understanding of formation evaluation, geology, spatial statistics, and machine learning but also become familiar with procedures for identifying research problems and conducting research to solve the problems. I am thankful for the life lessons I learned from them, which I believe would help me to further improve myself in my future work and life. I would also like to acknowledge all members of my dissertation committee, Dr. Larry Lake and Dr. Zoya Heidari, for helping review my work, and providing inspiring suggestions and comments.  \nI would like to acknowledge Rey Casanova, Amy Stewart, and Jin Lee for their administrative support. I am also grateful for the technical support I got from my computer specialists, Roger Terzian, Joaquín Ambía-Garrido, Bruce Klappauf, and John Cassibry.  \nI was happy that I could have the opportunity to work in three different groups and know about many great people. I would like to thank all my colleagues: Honggeun Jo, Javier Santos, Elnara Rustamzade, Tianqi Deng, Mahmood Shakiba, Mohamad Abdo, Haofeng Song, Lei Liu, Qianjun Liu, Frank Male, Bo Ren, Robin Dommisse, Pierre Aerens, Mohammad Albusairi, Mohamed Bennis, Lizhuo Li, Domenico Crisafulli, Cristian Dominguez, Ali Eghbali, Gabriel Gallardo Giozza, David Gonzalez, Jingxuan Liu, Wendi Liu, Tarek Mohamed, Daria Olszowska, Oriyomi Raheem, Misael Morales, Colin Schroeder, You Wang, Ivan Amaro, Midé Mabadeje, Camilo Gelvez, Julian Salazar,  \nZhonghao Sun, Jose Hernandez, Wilberth, Ayaz, David Medellin, Kyubo, Aymeric, Naif, Joshua, Juan Diego, Eduardo Maldonado, Hyungjoo, Mathilde, Elsa, Adam, Mauro, Vivek, Valeriia, Yanxiang Yu and many others. I also want to thank all my friends for their companion and support.  \nI would like to thank British Petroleum (BP) for the summer internship opportunities in 2020 and 2021. Special thanks to Obiajulu Isebor, for his mentorship during my internships. I am also grateful for the help I got from Anar Yusifov, Evgenia Polyakova, Kevin Wolf, Jingfeng Zhang, John Nawab, Jing Zhang, Johnston Rodney, Roy Atish, Sarita, Hayden, Glen Gettemy, Ganyuan Xia, Carole Decalf, Robert King, Patrick Connor, Matthew McQueen, Davies Brian, Nirjhor, Ning Yang, Grace Chan, Youssef Elkady. I would also like to thank Shell, Exxon Mobil, Chevron, Schlumberger, ComboCurves, Sinopec, CNPC, and QRI for offering me internship or full-time employment opportunities.  \nFinally, I would like to thank my parents and family for their unconditional love and support.  \nThe work reported in this dissertation was funded by the University of Texas at Austin’s Research Consortium on Formation Evaluation, Digital Reservoir Characterization Technology and Bureau of Economic Geology, jointly sponsored by Aramco, Baker Hughes, BHP Billiton, Exxon Mobil, BP, Shell, Co","cbCaii4aAyVgTmjb","https://ap.wps.com/l/cbCaii4aAyVgTmjb","pdf",10983606,1,240,"English","en",105,"# Dedication\n# Acknowledgements\n# Dissertation Overview\n## Geophysical Interpretation and Reservoir Modeling\n## Uncertainty Estimation and Decision-Making\n## Well-Log Interpretation and Machine-Learning Permeability Prediction","[{\"question\":\"What is the main purpose of reservoir description in this dissertation?\",\"answer\":\"To support decision-making in hydrocarbon resource development by connecting geophysical interpretation with reservoir modeling and simulation.\"},{\"question\":\"How does stochastic reservoir modeling contribute to uncertainty estimation?\",\"answer\":\"By using stochastic reservoir models conditioned on fluid production history to enable uncertainty estimation for hydrocarbon reserves and production forecasts.\"},{\"question\":\"Why are machine-learning approaches needed for permeability prediction?\",\"answer\":\"Conventional petrophysical models are often too simplistic to capture the complex relationships between well logs and rock properties, especially permeability, particularly in spatially complex rocks.\"}]","Reservoir Description via Statistical and Machine-Learning Approaches - 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