{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T13:54:20Z","timestamp":1779890060083,"version":"3.53.1"},"reference-count":0,"publisher":"Latvia University of Life Sciences and Technologies, Faculty of Engineering and Information Technologies","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,21]]},"abstract":"<jats:p>Wellbore wall collapse under complex geological conditions presents a significant challenge in oil well drilling, increasing repair costs and operational downtime. This study proposes a machine learning\u2013based approach to predict wellbore stability, developing a robust model utilizing geomechanical rock properties, drilling parameters, and geological data, with a binary target variable (1 for stable, 0 for unstable wells). A dataset of 5,000 records, including 200 collapse cases, was preprocessed \u2013 removing duplicates and missing values, handling outliers, normalizing numerical features, and encoding categorical variables \u2013 before being split into 80% training and 20% testing subsets. Gradient boosting (XGBoost) and random forest (Scikit-learn) were applied for binary classification, with hyperparameters optimized via GridSearchCV  gradient boosting outperformed random forest, achieving 93% accuracy, 89% recall, and 91% F1-score, compared to 91%, 87%, and 89%, respectively. The study recommends integrating the gradient boosting model into a real-time monitoring system, analyzing sensor data every 10 minutes to provide recommendations (e.g. increasing mud density or reducing drilling speed), potentially reducing collapses by 25%, cutting repair costs and downtime by 10%, and enhancing drilling efficiency. This research underscores machine learning potential to improve wellbore stability prediction, delivering significant economic and operational benefits to the oil and gas industry.   <\/jats:p>","DOI":"10.22616\/erdev.2025.24.tf109","type":"proceedings-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T11:42:07Z","timestamp":1748605327000},"source":"Crossref","is-referenced-by-count":5,"title":["Application of machine learning for wellbore stability assessment"],"prefix":"10.22616","volume":"24","author":[{"given":"Samal","family":"Muratova","sequence":"first","affiliation":[{"name":"Satbayev University, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oleksandr","family":"Pashchenko","sequence":"additional","affiliation":[{"name":"Dnipro University of Technology, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Volodymyr","family":"Khomenko","sequence":"additional","affiliation":[{"name":"Dnipro University of Technology, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abat","family":"Zhailiev","sequence":"additional","affiliation":[{"name":"Caspian University of Technology and Engineering named after Sh. Yessenov, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"9905","published-online":{"date-parts":[[2025,5,21]]},"event":{"name":"24th International Scientific Conference Engineering for Rural Development","acronym":"ERDev2025"},"container-title":["Engineering for Rural Development","24th International Scientific Conference Engineering for Rural Development Proceedings"],"original-title":[],"link":[{"URL":"https:\/\/www.iitf.lbtu.lv\/conference\/proceedings2025\/Papers\/TF109.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T12:06:00Z","timestamp":1748606760000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.iitf.lbtu.lv\/conference\/proceedings2025\/Papers\/TF109.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,21]]},"references-count":0,"URL":"https:\/\/doi.org\/10.22616\/erdev.2025.24.tf109","relation":{},"ISSN":["1691-5976"],"issn-type":[{"value":"1691-5976","type":"print"}],"subject":[],"published":{"date-parts":[[2025,5,21]]}}}