[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117885-en":3,"doc-seo-117885-105":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},117885,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Applications of Machine Learning in Reservoir Connectivity Detection and Optimization - A Dissertation","Reliable quantification of well connectivity is essential for understanding a reservoir and supporting future development plans such as rate optimization and selecting offset wells. The need becomes even more critical for high-capital projects including CO2 EOR and polymer floods. Traditional connectivity detection methods, such as tracer tests and streamline or numerical-simulation techniques, are either resource-intensive or computation-intensive. This dissertation presents machine-learning workflows for connectivity detection and rate optimization under geologic uncertainty.","APPLICATIONS OF MACHINE-LEARNING IN RESERVOIR CONNECTIVITY  \nDETECTION AND OPTIMIZATION  \nA Dissertation  \nby  \nDEEPTHI SEN  \nSubmitted to the Graduate and Professional School of Texas A&M University  \nin partial fulfillment of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nChair of Committee, Committee Members,  \nHead of Department,  \nAkhil Datta-Gupta Michael J King Eduardo Gildin Bani Mallick Jeff Spath  \nMay 2022  \nMajor Subject: Petroleum Engineering  \nCopyright 2022 Deepthi Sen  \nABSTRACT  \nReliable quantification of well connectivity is a crucial aspect in forming a good understanding of a reservoir, which in turn helps in formulating future development plans such as rate optimization and offset wells. This assumes an even greater importance when applied to high-capital projects such as CO2 EOR and polymer floods. Conventional methods for assessing well connectivity include tracer tests and numerical simulation-based techniques such as streamlines. However, these methods of connectivity detection tend to be either computation-intensive (i.e. numerical simulation) or resource-intensive (such as tracer tests) .  \nThis dissertation makes three major contributions related to machine-learning applications for connectivity detection and rate optimization. Firstly, I propose a novel approach for connectivity quantification and rate optimization during a waterflood under geologic uncertainty in reservoir properties such as permeability and porosity. A machine-learning (ML) based approach which is quick and scalable for rate optimization over multiple geologic realizations is proposed instead.  \nSecondly, a machine-learning framework is built on the statistical recurrent unit (SRU) model that interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. Furthermore, a streamline-based validation procedure is also proposed which provides physics-based backing to the results obtained from data analytics.  \nThirdly, this dissertation proposes a workflow that integrates unsupervised machine learning and streamline techniques to select representative geologic realizations based on their flow features. The workflow may be used to identify key wells for implementing optimized rate schedules, while taking into account the uncertainty in the geologic model.  \nACKNOWLEDGEMENTS  \nI would like to thank my committee chair, Dr. Akhil Datta-Gupta, and my committee members, Dr. Michael King, Dr. Eduardo Gildin, Dr. Bani Mallick for their guidance and support throughout the course of this research. I would also like to thank Dr. Debjyoti Banerjee for serving as a substitute committee member in place of Dr. Mallick during my final examination. I would like to express my sincere gratitude to Dr. Hongquan Chen for his mentorship and contribution to my research.  \nThanks also go to my closest friends – Dilly, Joshiba and Akshi, my colleagues and the department faculty and staff for making my grad school experience at Texas A&M University truly enjoyable.  \nFinally, thanks to my mother and father for their encouragement and to my husband Adi for his love and support.  \nCONTRIBUTORS AND FUNDING SOURCES  \nContributors  \nThis work was supervised by a dissertation committee consisting of Professors Akhil Datta-Gupta (advisor), Michael J. King and Eduardo Gildin of the Department of Petroleum Engineering and Professor Bani Mallick of the Department of Statistics.  \nThe streamline-tracing software used in Chapter II and rate optimization algorithm used in Chapter IV were developed by Dr. Hongquan Chen.  \nAll other work conducted for the dissertation was completed by the student independently.  \nFunding Sources  \nGraduate study was supported by Texas A&M University Joint Industry Projects (JIP), Model Calibration and Efficient Reservoir Imaging (MCERI) .  \nTABLE OF CONTENTS  \nPage  \nABSTRACT .......................................................................................................","cbCaimRk5QzY4OBR","https://ap.wps.com/l/cbCaimRk5QzY4OBR","pdf",10688616,1,158,"English","en",105,"# Abstract\n# Acknowledgements\n# Contributors and Funding Sources\n# Chapter I Introduction\n# Chapter II Data-Driven Rate Optimization under Geologic Uncertainty\n## Approach\n## Proxy model building\n## Rate Optimization Using Proxy Model\n## Mathematical Formulation\n# Chapter III Model-Free Assessment of Inter-well Connectivity using Statistical Recurrent Unit Models\n# Chapter IV Identification of Key Wells for Optimization Considering Geologic Uncertainty","[{\"question\":\"Why is reliable well connectivity quantification important in reservoir development?\",\"answer\":\"Reliable connectivity quantification helps build understanding of the reservoir and supports future development planning, including rate optimization and the selection of offset wells.\"},{\"question\":\"What limitations exist in conventional connectivity detection methods mentioned in the dissertation?\",\"answer\":\"Conventional approaches such as tracer tests and numerical simulation/streamline techniques are either resource-intensive (tracer tests) or computation-intensive (numerical simulation).\"},{\"question\":\"What are the three main contributions of this dissertation related to machine learning?\",\"answer\":\"It proposes an ML-based workflow for fast rate optimization during waterfloods under geologic uncertainty; it builds a statistical recurrent unit framework to infer inter-well connectivity from injection/production data without a geologic model; and it integrates unsupervised ML with streamline techniques to select representative geologic realizations for optimized scheduling.\"}]","Applications of Machine Learning in Reservoir Connectivity Detection and Optimization - A Dissertation | PDF",1785680149,398,{"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},"applications-of-machine-learning-in-reservoir-connectivity-detection-and-optimization-a-dissertation","",{"@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/applications-of-machine-learning-in-reservoir-connectivity-detection-and-optimization-a-dissertation/117885/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is reliable well connectivity quantification important in reservoir development?","Question",{"text":75,"@type":76},"Reliable connectivity quantification helps build understanding of the reservoir and supports future development planning, including rate optimization and the selection of offset wells.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitations exist in conventional connectivity detection methods mentioned in the dissertation?",{"text":80,"@type":76},"Conventional approaches such as tracer tests and numerical simulation/streamline techniques are either resource-intensive (tracer tests) or computation-intensive (numerical simulation).",{"name":82,"@type":73,"acceptedAnswer":83},"What are the three main contributions of this dissertation related to machine learning?",{"text":84,"@type":76},"It proposes an ML-based workflow for fast rate optimization during waterfloods under geologic uncertainty; 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