Development of compositional-based models for prediction of heavy crude oil viscosity: Application in reservoir simulations

Abstract

The properties of crude oil are of great importance for efficient recovery of oil from oil fields. The properties are primarily used in reservoir simulations for prediction of oil recovery in order to save time and obtain the best recovery. Among various crude oil properties, viscosity is the most important one which should be precisely simulated. In this work, a novel approach based on machine learning is developed for estimation of crude oil viscosity as function of input parameters. Multiple distinct tree-based ensemble models are applied on the available dataset in this work to predict heavy-oil viscosity. AdaBoost Decision Trees (ADA-DT), Random Forest (RF), and Extremely Randomized Trees (ERT) are selected tree-based ensembles that used in this work for the simulation of oil viscosity. An Isolation Forest is applied on the dataset to remove outliers and also the earthworm optimization algorithm (EWA) is employed to find the optimum values of models’ hyper-parameters. Optimized models of ADA-DT, ERT, and RF have RMSE error rates of 35.42, 27.02, and 58.71. Thus, ERT is selected as the best model of the dataset used in this work.