[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128070-en":3,"doc-seo-128070-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128070,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","MACHINE LEARNING APPROACHES FOR ENHANCED ANALYSES OF ROCK PROPERTIES IN GEOLOGIC CO2 STORAGE - Thesis","Geological carbon storage (GCS) is a promising strategy for mitigating atmospheric CO2 accumulation. This thesis applies machine learning (ML) to improve the analysis and optimization of CO2 storage operations by predicting key caprock performance metrics, including permeability and breakthrough pressure. After reviewing related work and developing robust ML models, the study evaluates three methods—non-linear neural networks, a Bayesian framework, and a classification model—using two-parameter and five-parameter input configurations. Results show strong agreement between predicted and measured breakthrough pressure, with the non-linear neural network performing best using five inputs. Beyond caprock prediction, ML is used to characterize fractured wellbore intervals from petrophysical and mineralogical parameters and to denoise seismic data while predicting first arrival time from acoustic emission waveforms, supporting improved sealing-capacity assessment and fracture detection.","MACHINE LEARNING APPROACHES FOR ENHANCED ANALYSES OF ROCK PROPERTIES IN GEOLOGIC CO2 STORAGE  \nBY  \nEKATERINA BARTENEVA  \nTHESIS  \nSubmitted in partial fulfillment of the requirements  \nfor the degree of Master of Science in Civil Engineering  \nin the Graduate College of the  \nUniversity of Illinois Urbana-Champaign, 2023  \nUrbana, Illinois  \nAdvisor:  \nAssistant Professor Roman Y. Makhnenko  \nABSTRACT  \nGeological carbon storage (GCS) has emerged as a promising approach for mitigating the accumulation of carbon dioxide (CO2) in the atmosphere. This research investigates the application of machine learning (ML) techniques to enhance the analysis and optimization of CO2 storage operations. The initial part of the study focuses on predicting permeability and breakthrough pressure in the caprock, the upper layer of GCS. By conducting a thorough literature review and developing robust ML algorithms, accurate predictions are achieved using input variables derived from the comprehensive datasets. Prediction of the CO2 breakthrough pressure is performed using three ML methods: the non-linear neural network, Bayesian framework, and the classification model are developed and evaluated for their accuracy and reliability. The analysis includes direct and indirect test results with two (porosity and permeability) and five (porosity, permeability, specific surface area, pore radius, and clay content) input parameter configurations. The results reveal strong correlations between the predicted and measured breakthrough pressure values. Thenon-linear neural network model demonstrates a superior performance when utilizing five input parameters, while the Bayesian framework yields identical predictions for both two-parameter and five-parameter configurations. The classification model successfully captures the correct range of values in its predictions for both configurations.  \nThe machine learning techniques are also utilized to analyze wellbore data and acoustic emission waveforms. Fractured wellbore intervals are predicted based on petrophysical and mineralogical parameters, underscoring the importance of considering multiple parameters for accurate fracture characterization. The analysis of acoustic emission waveforms demonstrates the effectiveness of deep learning models in denoising seismic data and accurately predicting the first arrival time. The findings of this study contribute to a better understanding of the correlation between the rock properties that are crucial for the analysis of the geologic CO2 storage. This work highlights the potential of ML approaches in robust assessment of caprock sealing capacity, enhancement of the quality of the seismic data interpretation, and efficient detection of the fractured intervals based on the wellbore log data.  \nACKNOWLEDGEMENTS  \nFirst and foremost, I would like to express my sincere gratitude to my research supervisor, Professor Roman Makhnenko. His unwavering support and dedicated involvement in every step of this thesis have been instrumental in its successful completion. I am deeply thankful for his guidance, understanding, and mentorship over the past two years.  \nI am grateful to the following organizations and funds for their generous support, which has been instrumental in the successful completion of my studies:  \n• Geotechnical Engineering Fellowship 2021/2022 at the CEE Department at UIUC.  \n• The U.S. Department of Energy project on \"Science-Informed Machine Learning to Accelerate Real-time (SMART) Decisions in Subsurface Applications Phase 2 – Development and Field Validation\".  \n• Professor Roman Makhnenko's Startup Fund at the CEE Department.  \nI would like to extend my heartfelt appreciation to the National Center for Supercomputing Applications (NCSA) for the access to computational resources, including a powerful GPU module (NSF grant \\#1725729) . The valuable feedback from NCSA researchers was critically important for my machine learning endeavors. I would also like to e","cbCaikwvBpRDK27G","https://ap.wps.com/l/cbCaikwvBpRDK27G","pdf",6427799,3,1,182,"English","en",105,"# Abstract\n# Acknowledgements\n## Caprock prediction\n## Wellbore and seismic analysis","[{\"question\":\"Which caprock properties are predicted using machine learning in this thesis?\",\"answer\":\"The study predicts permeability and CO2 breakthrough pressure in the caprock, using ML models trained on dataset-derived input variables.\"},{\"question\":\"How do the models compare under different input-parameter configurations?\",\"answer\":\"Three ML approaches are evaluated using two-parameter and five-parameter configurations. The non-linear neural network performs best with five inputs, while the Bayesian framework produces identical predictions for both configurations and the classification model captures the correct value ranges.\"},{\"question\":\"How are machine learning techniques used beyond caprock prediction?\",\"answer\":\"ML is applied to wellbore data to predict fractured intervals from petrophysical and mineralogical parameters, and to acoustic emission waveforms to denoise seismic signals and accurately estimate the first arrival time.\"}]","MACHINE LEARNING APPROACHES FOR ENHANCED ANALYSES OF ROCK PROPERTIES IN GEOLOGIC CO2 STORAGE - Thesis | PDF",1785944652,459,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-approaches-for-enhanced-analyses-of-rock-properties-in-geologic-co2-storage-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-approaches-for-enhanced-analyses-of-rock-properties-in-geologic-co2-storage-thesis/128070/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which caprock properties are predicted using machine learning in this thesis?","Question",{"text":76,"@type":77},"The study predicts permeability and CO2 breakthrough pressure in the caprock, using ML models trained on dataset-derived input variables.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do the models compare under different input-parameter configurations?",{"text":81,"@type":77},"Three ML approaches are evaluated using two-parameter and five-parameter configurations. The non-linear neural network performs best with five inputs, while the Bayesian framework produces identical predictions for both configurations and the classification model captures the correct value ranges.",{"name":83,"@type":74,"acceptedAnswer":84},"How are machine learning techniques used beyond caprock prediction?",{"text":85,"@type":77},"ML is applied to wellbore data to predict fractured intervals from petrophysical and mineralogical parameters, and to acoustic emission waveforms to denoise seismic signals and accurately estimate the first arrival time.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]