Principal component analysis (PCA) based hybrid models for the accurate estimation of reservoir water saturation

Abstract

Water saturation is imperative in the evaluation of hydrocarbon reserves available. However, it is challenging to accurately determine the water saturation of complex reservoirs using conventional techniques. This is due to the fact that the conventional models are unable to fully account for the heterogeneity of the reservoir and their results are highly influenced by factors such as type of data, approach and shale distribution. Moreover, most computational intelligence methods developed to estimate water saturation have neglected the relationship that can exist between input variables and their impact on model performance. This is because well log parameters can exhibit relationships among each other which leads to the presence of multiple collinearities and increases the complexity of the model. Therefore, this paper for the first time adopted principal component analysis (PCA) as a dimensionality reduction method to improve the performance of optimized least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system-subtractive clustering method (ANFIS-SCM). The experimental results clearly depicted a superior performance from PCA based LSSVM (PCA-LSSVM) during training and testing. Also, PCA minimized the overfitting experienced by ANFIS-SCM by improving the model's generalization ability. On the whole, PCA-LSSVM provided the least prediction error and outperformed PCA based ANFIS-SCM (PCA-ANFIS-SCM) when estimating water saturation.