[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127671-en":3,"doc-seo-127671-105":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},127671,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Kuwaiti Carbonate Reservoir Oil Recovery Prediction Through Static Wettability Contact Angle Using Machine Learning Modeling - Conference Paper","Study objective is to predict EOR recovery efficiencies using static wettability contact angle measurements coupled with machine learning modeling. Instead of conventional contact-angle measurement, digital static wettability contact angle is captured from reservoir imaging and modeled to forecast recovery driven by wettability distribution. Morphological-driven pixel data and transformed numerical contact-angle variables are derived from SEM-BSE images for 27 fresh core samples, followed by data science lifecycle steps for mining, exploration, training, and testing. Supervised models are evaluated; XGBoost provides the best-fit performance, supporting improved field development decisions based on wettability and pore morphology.","Missouri University of Science and Technology  \nScholars' Mine  \n\n| Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works | Geosciences and Geological and Petroleum Engineering |\n| --- | --- |\n| 01 Jan 2023\u003Cbr>Kuwaiti Carbonate Reservoir Oil Recovery Prediction Through Static Wettability Contact Angle Using Machine Learning Modeling\u003Cbr>Saleh Al-Sayegh\u003Cbr>Ralph E. Flori\u003Cbr>Missouri University of Science and Technology, [reflori@mst.edu](reflori@mst.edu)[ ](reflori@mst.edu)[Waleed Hussien Al-Bazzaz](Waleed Hussien Al-Bazzaz)\u003Cbr>Hasan Al-Saedi\u003Cbr>[et. al. For a complete list of authors](et. al. For a complete list of authors), see [https://](https://)scholarsmine. mst. edu/geosci_geo_peteng_facwork/2213\u003Cbr>Follow this and additional works at: [https://scholarsmine.mst.edu/geosci_geo_peteng_facwork](https://scholarsmine.mst.edu/geosci_geo_peteng_facwork)\u003Cbr> Part of the Geology Commons, and the Mining Engineering Commons |  |\n\nRecommended Citation  \nS. Al-Sayegh et al., \"Kuwaiti Carbonate Reservoir Oil Recovery Prediction Through Static Wettability Contact Angle Using Machine Learning Modeling,\" Society of Petroleum Engineers-SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition, APOG 2023, Society of Petroleum Engineers, Jan 2023. The definitive version is available at [https://doi.org/10.2118/215260-MS](https://doi.org/10.2118/215260-MS)  \nThis Article-Conference proceedings is brought to you for free and open access by Scholars' Mine. It has been accepted for inclusion in Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works by an authorized administrator of Scholars' Mine. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [scholarsmine@mst.edu](scholarsmine@mst.edu).  \nSPE-215260-MS  \nKuwaiti Carbonate Reservoir Oil Recovery Prediction Through Static Wettability Contact Angle Using Machine Learning Modeling  \nSaleh Al-Sayegh, Missouri University of Science and Technology / Kuwait Oil Company; Ralph Flori, Missouri University of Science and Technology; Waleed Hussien Al-Bazzaz, Kuwait Institute for Scientific Research; Hasan  \nAl-Saedi, Missouri University of Science and Technology; Mostafa Al-Kaouri and Ali Qubian, Kuwait Oil Company Copyright 2023, Society of Petroleum Engineers DOI 10.2118/215260-MS  \nThis paper was prepared for presentation at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition held in Jakarta, Indonesia on 10 – 12 October, 2023.  \nThis paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s) . Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s) . The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.  \nAbstract  \nThe objective of this study is to predict EOR efficiencies through static wettability contact angle measurement by Machine Learning (ML) modeling. Unlike conventional methods of measuring static wettability contact angle, the unconventional digital static wettability contact angle is captured and measured, then (ML) modeled in order to forecast the recovery based on wettability distribution phenomenon. Due to success in big data collection from reservoir imaging samples, this study applies data science lifecycle logic and utilizes Machine Learning (ML) models that can predict the recovery through wetta","cbCair503fGgR2wi","https://ap.wps.com/l/cbCair503fGgR2wi","pdf",2623320,3,1,18,"English","en",105,"# Abstract\n## Objective and Approach\n## Data Acquisition and Processing\n## Modeling and Model Selection\n## Results and Practical Value","[{\"question\":\"How does the study predict oil recovery efficiency?\",\"answer\":\"It predicts EOR efficiencies by modeling recovery outcomes using static wettability contact angle measurements captured digitally from reservoir imaging.\"},{\"question\":\"What data sources and sample set are used for modeling?\",\"answer\":\"SEM-BSE images are used to generate morphological-driven pixel data and transformed numerical wettability contact angle variables from 27 fresh core samples sequenced from top to bottom of the reservoir.\"},{\"question\":\"Which machine learning model performed best and what accuracy was achieved?\",\"answer\":\"Extreme Gradient Boosting (XGBoost) was identified as the best-fit model with an acceptable accuracy, while decision tree and ANN models provided acceptable but less optimal performance.\"}]","Kuwaiti Carbonate Reservoir Oil Recovery Prediction Through Static Wettability Contact Angle Using Machine Learning Modeling - Conference Paper | PDF",1785940653,45,{"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},"kuwaiti-carbonate-reservoir-oil-recovery-prediction-through-static-wettability-contact-angle-using-machine-learning-modeling-conference-paper","",{"@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/kuwaiti-carbonate-reservoir-oil-recovery-prediction-through-static-wettability-contact-angle-using-machine-learning-modeling-conference-paper/127671/",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","2026-08-23","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},"How does the study predict oil recovery efficiency?","Question",{"text":76,"@type":77},"It predicts EOR efficiencies by modeling recovery outcomes using static wettability contact angle measurements captured digitally from reservoir imaging.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data sources and sample set are used for modeling?",{"text":81,"@type":77},"SEM-BSE images are used to generate morphological-driven pixel data and transformed numerical wettability contact angle variables from 27 fresh core samples sequenced from top to bottom of the reservoir.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning model performed best and what accuracy was achieved?",{"text":85,"@type":77},"Extreme Gradient Boosting (XGBoost) was identified as the best-fit model with an acceptable accuracy, while decision tree and ANN models provided acceptable but less optimal performance.","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"]