Integration of field data and application of machine learning methods to assess the condition of the near-wellbore zone of carbonate reservoirs

Abstract

Relevance. Increase in the share of hard-to-recover reserves. Often, for the effective development of complex reservoirs, methods of enhanced oil recovery and production intensification are used. Currently, the feasibility of carrying out geological and technical measures is based on the results of interpretation well tests, which allows assessing the condition of the near-wellbore zone. The disadvantages of this research method are a long shutdown (as a result, “shortfalls” of oil) and increased risks of failure to bring wells into operation. In this regard, the integration of field data and the use of machine learning to describe the state of the near-wellbore zone can have a positive effect on the timeliness of geological and technical activities and ensure maximization of their effectiveness in the future. Aim. To develop a methodology for increasing the accuracy of the near-wellbore zone permeability prediction of carbonate reservoirs based on the use of machine learning methods. Methods. Statistical methods, solving the classification problem using machine learning methods. Results. This paper proposes an approach for quickly assessing the permeability of the near-wellbore zone, based on a statistical analysis of the results of interpretation of hydrodynamic studies (256 studies) and operational data from wells of an oil carbonate reservoir in the Perm Krai. To assess near-wellbore zone permeability, a multiple linear regression model was built. In order to improve the statistical metrics of regression of the near-wellbore zone permeability, the dependence of this parameter on the specific productivity coefficient in the conditions of a carbonate reservoir was studied and divided into clusters. The SHAP library was used to identify significant parameters on the predicted value. To perform the task of classifying clusters based on source data, the authors have used a machine learning technique – support vector machine and constructed differentially the regression models for each cluster. Using of this approach made it possible to increase the coefficient of determination from 0.76 to 0.96 and reduce the average absolute error in predicting the near-wellbore zone permeability from 0.018 to 0.007 µm2. Thus, the authors proposed a methodology for predicting the near-wellbore zone permeability using statistical methods based on preliminary clustering of the initial data and their classification using machine learning approaches.

Keywords

• carbonate reservoir
• machine learning
• support vector machine
• permeability
• near-wellbore zone