A PREDICTIVE MODEL FOR OIL WELL MAINTENANCE: A CASE STUDY IN KAZAKHSTAN
Aktaukenov D. Alshaalan M. Omirbekova Z. Pinsky E.
2024Oil Gas Scientific Research Project Institute
SOCAR Proceedings
2024Issue 148 - 56 pp.
This paper proposes a predictive model to help oil workers build a reliable model for identifying oilwell failures. It can help geologists experienced with Machine Learning to improve the accuracy of failure identification and a more accurate approach to well-maintenance planning. This study is based on output data statistics such as per-well daily oil flowmeter readings. The volatility of these indications makes it possible to determine the probability of an oilwell failure. This method makes it possible to rank wells according to the principle of the most probable failures for workers making decisions. The use of predictive diagnostics can help to detect equipment problems early, thereby minimizing unplanned downtime. Unplanned sudden oilwell failures increase the company’s operating costs, as well as increase risks of environmental pollution.
classification algorithms , decision-making , machine learning , oil and gas , prediction algorithms
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Satbayev University, Almaty, Kazakhstan
Metropolitan College, Boston University, Boston, MA, United States
Kazakh National University, after Al-Farabi, Almaty, Kazakhstan
Satbayev University
Metropolitan College
Kazakh National University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026