ФОРМИРОВАНИЕ И РЕДУКЦИЯ БАЗЫ ЗНАНИЙ ДЛЯ ОЦЕНКИ ЦЕЛЕСООБРАЗНОСТИ БУРЕНИЯ СКВАЖИН НА НЕФТЯНЫХ МЕСТОРОЖДЕНИЯХ

Abstract

The article discusses the issues of forming and reducing a fuzzy knowledge base designed to assess the feasibility of drilling wells in oil fields as part of a decision support system. The initial knowledge base was formed using a specially developed software package that implements neuro-fuzzy modeling technology based on a group of fuzzy neural networks. To train them and form a knowledge base, initial data were required that described the dependence of the influence of the characteristics of producing wells on their efficiency in terms of the production rate of oil produced. The initial data for the analysis of 3187 records were presented for 46 oil field objects with carbonate reservoirs. Each record was characterized by the values of the input parameters “absolute rock permeability”, “rock porosity”, “average flow rate of neighboring wells” and “minimum water cut of neighboring wells”. The output parameter determined the efficiency of the well as the average oil flow rate for the first 3 years of operation. When training a team of 5 fuzzy neural networks, 5 gradations of input parameters were selected. The total number of fuzzy rules in the original knowledge base was 319. To assess its classification ability, the values of the Accuracy, Precision, Recall and F1-Score metrics were calculated. To reduce the knowledge base, a specially developed software package was used that implements 2 stages of genetic optimization: structural-parametric transformations of the fuzzy rules of the original knowledge base to obtain an intermediate knowledge base and minimizing the composition of the fuzzy rules of the intermediate knowledge base to obtain the desired (reduced) knowledge base. As a result of the reduction, the quantitative composition of knowledge bases, as well as the values of classification quality metrics, changed. The number of rules in the intermediate knowledge base was 131, and in the reduced one - 33. The overall reduction in the volume of the knowledge base was 89.66%. After the 1st stage of reduction, the values of the Accuracy, Precision, Recall and F1-Score metrics on the test data set increased, respectively, by 7.88%, 6.8%, 0.34% and 0.53%. After the 2nd stage of reduction, the overall increase in the values of these metrics was, respectively, 9.02%, 7.55%, 1.58% and 1.68%. The results obtained were tested at Tatneft and used to form and optimize the knowledge base when developing a decision support system for assessing the feasibility of drilling wells in oil fields.