Article · Proceedings of OilGasScientificResearchProjects Institute SOCAR · March 30, 2024
A predictive model for oil well maintenance: a case study in Kazakhstan
Daur Aktaukenov, M. Alshaalan, Zhanar Zh. Omirbekova, Eugene Pinsky
Abstract
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. Keywords: machine learning; classification algorithms; oil and gas; prediction algorithms; decision-making.
More by Zhanar Omirbekova
- Hybrid Intelligent Control of Electrostatic Precipitators Based on MIMO Modeling and Fuzzy LogicInternational Conference on Control and Automation · 2026
- Intelligent Real-Time Control of Electrostatic Precipitators based on Neural Network ModelingInternational Conference on Control and Automation · 2026
- Clustering analysis for predictive maintenance of oil wells in KazakhstanProceedings of OilGasScientificResearchProjects Institute SOCAR · 2026
- Design, simulation, and analysis of a solar-powered street lighting control system for power consumption predictionInternational Journal of Innovative Research and Scientific Studies · 2025