Abstract
In the development of brownfields, various geological and technological complications can arise. To enhance the smooth operation of downhole pumping equipment, companies implement a range of methods and techniques. This article analyzes the potential of using machine learning to improve the reliability of underground well equipment in the fields of NC KazMunayGas JSC. The research focuses on the development and validation of predictive models that accurately forecast potential downhole equipment failures. It thoroughly analyzes existing machine learning methods, approaches and their real-life application, highlighting key success factors and limitations. The results of the study demonstrate the significant potential for using a well failure prediction model when selecting the optimal machine learning approach to reduce unscheduled downtime and optimize well maintenance processes. The authors assessed the potential for using failure prediction techniques for downhole pumping equipment in wells that utilizes sucker rod pumps. Implementing failure prediction techniques for downhole pumping equipment can help ensure uninterrupted well operation by minimizing well failures and reducing downtime for repairs.