Permeability Prediction and Facies Distribution for Yamama Reservoir in Faihaa Oil Field: Role of Machine Learning and Cluster Analysis Approach

Abstract

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, FH3, and FH19 from the Yamama reservoir in the Faihaa Oil Field, southern Iraq. The framework includes: calculating permeability for uncored wells using the classical method and FZI method. Topological mapping of input space into clusters is achieved using the self-organizing map (SOM), as an unsupervised machine-learning technique. By leveraging data obtained from the four wells, the SOM is effectively employed to forecast the count of electrofacies present within the reservoir. According to the findings, the permeability calculated using the classical method that relies exclusively on porosity is not close enough to the actual values because of the heterogeneity of carbonate reservoirs. Using the FZI method, in contrast, displays more real values and offers the best correlation coefficient. Then, the SOM model and cluster analysis reveal the existence of five distinct groups.