[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119659-en":3,"doc-seo-119659-105":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":20,"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},119659,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Integrated Reservoir Simulation and Machine Learning for Enhanced Reservoir Characterization and Performance Prediction - Thesis Study","This thesis delivers an integrated workflow combining reservoir simulation and machine learning to strengthen reservoir characterization and performance prediction. Using the Sarir C-Main field, it integrates seismic cubes, well logs, base maps, check-shot data, and production history to build static and dynamic models, including interpretation, gridding, domain conversion, and property and petrophysical modeling. History matching and prediction are executed through simulation cases, while DTW and LSTM are applied for oil production forecasting. Results from Petrel simulation support effective depletion strategy, history matching, and completion, and DTW/LSTM show reliable performance with faster runtimes. Transfer learning improves accuracy under limited data.","Integrated Reservoir Simulation and Machine Learning for Enhanced Reservoir Characterization and Performance Prediction  \nBy  \n© Mohammed Otmane  \nA thesis submitted to the School of Graduate Studies in partial fulfillment of the requirements  \nfor the degree of  \nMaster of Engineering  \nDepartment of Process Engineering  \nMemorial University of Newfoundland  \nOctober 2024  \nSt. John’s, Newfoundland and Labrador  \nAbstract  \nThis thesis presents a comprehensive study on reservoir simulation and machine learning techniques for improved understanding and prediction of reservoir behavior. The research focuses on the Sarir C-Main field and utilizes various data sources including seismic cubes, well logs, base maps, check shot data, and production history. The methodology involves the development of static and dynamic models through processes such as data quality control, log interpretation, seismic interpretation, horizon and surface interpretation, fault interpretation, gridding, domain conversion, property and petrophysical modeling. Additionally, well completion, fluid model definition, and rock physics functions are established. History matching and prediction are performed using simulation cases, and machine learning techniques including data gathering, cleaning, dynamic time warping (DTW), long short-term memory (LSTM), and transfer learning are applied. The results obtained through Petrel simulation demonstrate the effectiveness of depletion strategy, history matching, and completion in capturing reservoir behavior. Furthermore, machine learning techniques, specifically DTW and LSTM, exhibit promising results in predicting oil production. The study concluded that machine learning approaches, such as the LSTM model, offer distinct advantages. They require significantly less time and can yield reliable predictions. By leveraging the power of transfer learning, accurate predictions can be achieved efficiently when limited data are available, offering a more streamlined and practical alternative to traditional reservoir simulation methods.  \nAcknowledgements  \nI would like to express my deepest gratitude to all those who have contributed to the successful completion of this thesis.  \nFirst and foremost, I would like to thank my supervisors Dr. Amer Aborig and Dr. Syed Imtiaz for their guidance, support, and invaluable insights throughout the research process. Their expertise and encouragement have been instrumental in shaping the direction of this study.  \nI would also like to extend my sincere appreciation to the faculty members of the Engineering and Applied Science department at Memorial University of Newfoundland for their knowledge, expertise, and encouragement. Their commitment to academic excellence has been a constant source of motivation.  \nI am grateful to Mr. Ashraf Elferjani and Dr. Adel Elzwai from Arabian Gulf Oil Company for providing access to the necessary data and resources. Their cooperation and assistance have been invaluable in conducting this research.  \nI would like to thank my family and friends for their unwavering support and understanding during this journey. Their encouragement and belief in me have been a constant source of strength. Finally, I would like to express my heartfelt gratitude to all the participants who generously shared their time and expertise without their contributions, this research would not have been possible.  \nTable of Contents  \nAbstract ............................................................................................................................................ i  \nAcknowledgements ......................................................................................................................... ii  \nTable of Contents ........................................................................................................................... iii  \n[List of Tables ................................................................................................","cbCaityOIpHlA2FS","https://ap.wps.com/l/cbCaityOIpHlA2FS","pdf",12119931,1,129,"English","en",105,"# Abstract\n# Acknowledgements\n# Table of Contents\n# List of Tables\n# List of Figures\n# List of Abbreviations and Symbols\n# 1. Introduction\n## 1.1 Objectives\n# 2. Literature Review\n## 2.1 Reservoir Simulation\n## 2.2 Machine Learning\n# 3. Methodology\n## 3.1 Case Study\n## 3.2 Sarir C-Main Petrophysics Data\n## 3.2.1 Seismic Cube\n## 3.2.2 Well Logs Data\n## 3.2.3 Base Map","[{\"question\":\"What reservoir case study is used in the thesis?\",\"answer\":\"The study focuses on the Sarir C-Main field and uses multiple data sources such as seismic cubes, well logs, base maps, check-shot data, and production history.\"},{\"question\":\"How are machine learning methods applied for prediction?\",\"answer\":\"Machine learning is used alongside simulation to perform history matching and predict performance, applying DTW and LSTM for forecasting oil production.\"},{\"question\":\"What advantages does the thesis report for machine learning models?\",\"answer\":\"The thesis concludes that approaches such as LSTM can produce reliable predictions with significantly less time, and transfer learning enables accurate results even when data are limited.\"}]","Integrated Reservoir Simulation and Machine Learning for Enhanced Reservoir Characterization and Performance Prediction - Thesis Study | PDF",1785725535,325,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"integrated-reservoir-simulation-and-machine-learning-for-enhanced-reservoir-characterization-and-performance-prediction-thesis-study","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/integrated-reservoir-simulation-and-machine-learning-for-enhanced-reservoir-characterization-and-performance-prediction-thesis-study/119659/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What reservoir case study is used in the thesis?","Question",{"text":75,"@type":76},"The study focuses on the Sarir C-Main field and uses multiple data sources such as seismic cubes, well logs, base maps, check-shot data, and production history.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are machine learning methods applied for prediction?",{"text":80,"@type":76},"Machine learning is used alongside simulation to perform history matching and predict performance, applying DTW and LSTM for forecasting oil production.",{"name":82,"@type":73,"acceptedAnswer":83},"What advantages does the thesis report for machine learning models?",{"text":84,"@type":76},"The thesis concludes that approaches such as LSTM can produce reliable predictions with significantly less time, and transfer learning enables accurate results even when data are limited.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]