Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods.

Precise estimation of rock petrophysical parameters are seriously important for the reliable computation of hydrocarbon in place in the underground formations. Therefore, accurately estimation rock saturation exponent is necessary in this regard. In this communication, we aim to develop intelligent data-driven models of decision tree, random forest, ensemble learning, adaptive boosting, support vector machine and multilayer perceptron artificial neural network to predict rock saturation exponent parameter in terms of rock absolute permeability, porosity, resistivity index, true resistivity, and water saturation based on acquired 1041 field data. A well-known outlier detection algorithm is applied on the gathered data to assess the data reliability before model development. Additionally, relevancy factor is estimated for each input parameter to assess the relative effects of input parameters on the saturation exponent. The sensitivity analysis indicates that resistivity index and true resistivity have direct correlation with the saturation exponent while porosity, absolute permeability and water saturation is inversely related with saturation exponent. In addition, the graphical-based and statistical-based evaluations illustrate that AdaBoost and ensemble learning models outperforms all other developed data-driven intelligent models as these two models are associated with lowest values of mean square error (adaptive boosting: 0.017 and ensemble learning: 0.021 based on unseen test data) and largest values of coefficient of determination (adaptive boosting: 0.986 and ensemble learning: 0.983 based on unseen test data).