[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120182-en":3,"doc-seo-120182-105":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},120182,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning-Driven Quantification of CO₂ Plume Dynamics at IBDP Sites using Microseismic Data","This thesis investigates machine learning methods to quantify the spatial extent of CO₂ plumes using microseismic data from the Illinois Basin Decatur Project (IBDP) between November 2011 and June 2018. The work leverages well logs, microseismic activity, and CO₂ injection metrics to predict the temporal evolution of subsurface CO₂ saturation. Results show plume behavior with vertical clustering near the injection well, periodic migration consistent with an invasion percolation pattern, and intermittent breaching of baffles that act as leaky seals.","Graduate Theses, Dissertations, and Problem Reports  \n2024  \nMachine Learning-Driven Quantification of CO₂ Plume Dynamics at IBDP Sites using Microseismic Data  \nIkponmwosa Bright Iyegbekedo  \nWest Virginia University  \nFollow this and additional works at: [https://researchrepository.wvu.edu/etd](https://researchrepository.wvu.edu/etd)  \n Part of the Other Engineering Commons  \nRecommended Citation  \nIyegbekedo, Ikponmwosa Bright, \"Machine Learning-Driven Quantification of CO₂ Plume Dynamics at IBDP Sites using Microseismic Data\" (2024) . Graduate Theses, Dissertations, and Problem Reports. 12525.  \n[https://researchrepository.wvu.edu/etd/12525](https://researchrepository.wvu.edu/etd/12525)  \nThis Thesis is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s) . You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact [researchrepository@mail.wvu.edu](researchrepository@mail.wvu.edu).  \nMachine Learning-Driven Quantification of CO₂ Plume Dynamics at IBDP Sites  \nusing Microseismic Data  \nIyegbekedo Ikponmwosa Bright  \nThesis submitted  \nTo the Benjamin M. Statler College of Engineering at West Virginia University  \nin partial fulfillment of the requirements for the degree of  \nMaster of Science in  \nPetroleum and Natural Gas Engineering  \nEbrahim Fathi, Ph.D., Chair  \nSamuel Ameri, Professor  \nKashy Aminian, Ph.D.  \nDepartment of Petroleum and Natural Gas Engineering  \nMorgantown, West Virginia  \n2024  \nKeywords: CO₂ sequestration, Illinois Basin Decatur Project (IBDP) , Mt. Simon Sandstone, Buoyancy effect, CO₂ plume dynamics.  \nCopyright© 2024  \nIyegbekedo Ikponmwosa Bright  \nABSTRACT  \nMachine Learning Quantification of CO₂ Plume Extension at IBDP Sites through  \nMicroseismic Data Analysis  \nIyegbekedo Ikponmwosa Bright  \nThis thesis delves into the utilization of machine learning methodologies to quantify the spatial extent of CO₂ plumes by leveraging microseismic data obtained from the Illinois Basin Decatur Project (IBDP) site spanning November 2011 to June 2018. This initiative, focused on the geological sequestration of carbon dioxide, furnishes a unique and comprehensive dataset comprising well logs, microseismic activity records, and CO₂ injection metrics, all crucial for quantifying the subsurface CO₂ saturation plume dynamics. The primary objective is to forecast the temporal evolution of CO₂ saturation plumes in the subsurface, a critical undertaking for ensuring both the environmental integrity and operational efficacy of CO₂ sequestration activities. The findings reveal that the application of machine learning for interpreting microseismic data can forecast plume behavior exhibiting vertical clustering within a confined range of distances from the injection well, indicative of periodic migration and following an invasion percolation model. The buoyant CO₂ plume is partly trapped within the sandstone intervals periodically breaching discrete barriers or baffles. This observation aligns with earlier investigations that uncovered the presence of cemented or shale-rich intra-formational baffles. These intervals act as leaky seals impeding the vertical migration of injected CO₂ into the Mt. Simon sandstone, confining it within thin, highly saturated layers until buoyancy overcomes gravity and capillary forces, leading to periodic breakthroughs along vertical zones of weakness. By employing clustering algorithms such as K-Means and DBSCAN, we were ","cbCaidm49JGTE4Gw","https://ap.wps.com/l/cbCaidm49JGTE4Gw","pdf",3329414,1,71,"English","en",105,"# Abstract\n## Data Sources and Scope\n## Modeling Approach and Clustering Methods\n## Key Findings and Interpretation\n## Significance for Monitoring and Management","[{\"question\":\"What data does the thesis use to model CO₂ plume dynamics at the IBDP sites?\",\"answer\":\"It uses microseismic activity records collected at the Illinois Basin Decatur Project (IBDP), along with well logs and CO₂ injection metrics to support plume quantification and saturation dynamics.\"},{\"question\":\"How do the machine learning models characterize CO₂ plume extension?\",\"answer\":\"By applying clustering algorithms such as K-Means and DBSCAN, the study identifies seismic patterns and trends to estimate plume expansion both vertically and horizontally.\"},{\"question\":\"What does the thesis conclude about how the CO₂ plume migrates?\",\"answer\":\"The plume primarily expands vertically within the Mt. Simon B and C formations, showing significant vertical migration during the injection phase, while horizontal migration is less pronounced but still detectable.\"}]","Machine Learning-Driven Quantification of CO₂ Plume Dynamics at IBDP Sites using Microseismic Data | PDF",1785728589,179,{"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},"machine-learning-driven-quantification-of-co-plume-dynamics-at-ibdp-sites-using-microseismic-data","",{"@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/machine-learning-driven-quantification-of-co-plume-dynamics-at-ibdp-sites-using-microseismic-data/120182/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data does the thesis use to model CO₂ plume dynamics at the IBDP sites?","Question",{"text":75,"@type":76},"It uses microseismic activity records collected at the Illinois Basin Decatur Project (IBDP), along with well logs and CO₂ injection metrics to support plume quantification and saturation dynamics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the machine learning models characterize CO₂ plume extension?",{"text":80,"@type":76},"By applying clustering algorithms such as K-Means and DBSCAN, the study identifies seismic patterns and trends to estimate plume expansion both vertically and horizontally.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the thesis conclude about how the CO₂ plume migrates?",{"text":84,"@type":76},"The plume primarily expands vertically within the Mt. 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