IDENTIFICATION OF CAUSES FOR DECLINE IN OIL PRODUCTION DUE TO INCREASED WATER CUT IN MATURE FIELDS

The article is focused on identifying the causes of reduced oil production rates due to increased water cut at AB1(1-2) object of a mature field. It classifies and analyzes the existing methods for determining the causes of rising water cut, including instrumental, hydrodynamic and field-analytical approaches. The study distinguishes potentially reversible and irreversible water cut losses, providing real examples to illustrate the current challenges in the oil production industry. The paper underscores the necessity of developing a machine learning-based tool to enhance process efficiency and presents a development plan for such instrumentation. The development of tools for determining the causes of flooding of oil wells using integrated analytical methods and machine learning technologies is an important step towards improving the efficiency of oil production. Combining various approaches in a single algorithm will significantly optimize the time for decisionmaking and reduce losses in oil production. The implementation of such technology will be the key to successfully solving the problem of determining the causes of flooding. The key element in creating an effective toolkit will be the implementation of machine learning technologies. Training on historical data will allow the algorithm to identify complex dependencies and patterns that may not be obvious in traditional analysis. Machine learning models will be able to adapt to changes in data, which will make them more flexible and accurate in predicting the causes of flooding. The use of machine learning will also allow the creation of predictive models that will not only determine the current causes of flooding, but also predict possible problems in the future. This will make it possible to take preventive measures and optimize the production process.