Automation of workover candidate ranking processes at Krasnoleninskoye oil and gas condensate field

Abstract

Background. Workovers (WO) are the main EOR tool at Krasnoleninskoye reservoirs. Therefore, the issue of increasing the reliability of technological and economic performance when planning various types of workovers is urgent. This is due to the complexity of selecting well candidates, the lack of a comprehensive methodology for assessing the short-term and long-term potential of wells, large WO scopes, as well as declining WO performance associated with the reduction of reserves, deterioration of the energy state of the reservoirs, and advancement of the injected water front. The purpose of the study is to create mathematical tools that will reduce the time of well-candidates selection for various types of workovers and to improve the WO quality for entire field. The paper describes methods of automated selection of well candidates that were successfully applied in the conditions of the field of interest, namely graphical and mathematical tools. The mathematical one has been created based on the correlation-regression analysis of the actual implementation of stimulation methods in various geological-field conditions in Microsoft Excel 2010 with Visual Basic for Applications (VBA). The graphical tool has been generated on the basis of all historical field data verified and processed using methods of primary statistical analysis in RN-KIN software. The study resulted in a technique that was selected and tested in the conditions of Krasnoleninskoye oil and gas condensate field. The process of introducing the developed approaches to the search for well candidates for various types of workovers in the field was accompanied by updating, analysis of results, and cyclic training of the system. A methodological approach has been developed, including the combination of several methods for selecting well candidates for various types of workovers. A combination of statistical and graphical methods made it possible to significantly improve the reliability of WO candidates selection and therefore to reduce the share of uneconomic workovers by 12 % in the period from 2017 to 2020. As part of the study, a script has been developed that automatically computes the rank of a well-candidate which significantly reduces time costs and allows to quickly evaluate the “best” workover candidates.