THE CONSTRUCTION OF INTELLIGENT RULE-BASED SYSTEM WITH DEEP-LEARNING FUNCTIONALITY FOR OIL AND GAS WELLS DRILLING

Abstract

The article is devoted to solving the problems of predictive modeling using machine learning methods under conditions of uncertainty in huge data sets created in various sectors of the economy, in particular, the oil and gas industry, due to the spread of IoT and cloud services. The author notes that existing machine learning methods, including widely used deep learning methods, face limitations in achieving high prediction accuracy due to various uncertainties. The article explores fuzzy learning approaches to overcome uncertainties such as imprecision and ambiguity, but emphasizes their limitations of incompleteness and vagueness. It is argued that rule-based expert systems augmented with deep learning offer a more robust framework for dealing with such uncertainties by integrating associative memory, helping to produce more accurate predictive models, especially in data-intensive scenarios such as oil and gas drilling processes.  The study focuses on improving predictive modeling under conditions of uncertainty in large data sets. We discuss the limitations of fuzzy learning when processing incomplete data and propose a hybrid approach to the problem. Integration of the associative memory learning method into the logical inference procedures allows to identify accurate data models, thereby improving the quality of forecasting under uncertainty. This integrated approach aims to improve forecasting accuracy by leveraging the strengths of deep learning in pattern recognition and the ability of rule-based systems to cope with uncertainty. The main result of the research work is the development of a new method that integrates a level of deep processing with associative memory into a rule-based system to improve forecasting accuracy under uncertainty. This was achieved by using a multilayer neural network and adding additional parameters. This method significantly improved the accuracy of predicting the dynamics of operational parameters during drilling. Thus, the presented study is a new contribution to the task of developing forecasting methods under conditions of uncertainty, which is especially relevant for oil and gas drilling.