Modeling and Adoption of Technological Solutions in Order to Enhance the Effectiveness of Measures to Limit Water Inflows into Oil Wells under Conditions of Uncertainty

This article is devoted to the construction and statistical analysis of models that express the relationship between performance indicators and a large number of geological and technological factors. The volume of additionally produced oil, the volume of limited water, the duration of the effect and profit per well, taking into account the cost of the polymer, are taken as performance indicators. The key goal of the article is to develop a method and models for making technological choices to enhance the effectiveness of measures to limit water inflows in production wells under conditions of uncertainty. The methodological basis of the study was the provisions and principles of mathematical statistics, the theory of fuzzy sets, the theory of decision-making under conditions of uncertainty based on materials generated by statistical processing of data on physical and geological conditions, and the results of waterproofing work, obtaining, and analyzing information. The scientific novelty of the study lies in the construction of technological solutions based on modeling the performance indicators of waterproofing works with an assessment of the significance of each factor and the reliability of the models and decision-making under conditions of uncertainty, expressed by multi-criteria and multi-factoriality. The practical significance follows from a solution that satisfies the conditions for achieving the maximum of all indicators of the efficiency of the process of limiting water inflows, both technological and economic. An algorithm was developed and implemented for evaluating optimal technological solutions according to four criteria based on information about the geological and physical conditions of the field and the experience of implementing geological and technical measures to limit water inflows, including the analysis of factors, their weighted contribution, model building, statistical evaluation of reliability indicators, decision-making taking into account uncertainty.