Multiple machine learning models in estimating viscosity of crude oil: Comparisons and optimization for reservoir simulation

Abstract

Development of predictive models for estimation of reservoir fluids properties as function of temperature, fluid composition, and pressure would be essential for simulation of oil reservoirs. The measurement of the viscosity of heavy crude oil is an essential part of petroleum science which can be done by experimental methods, however computational methods can be integrated to the experimental methods to reduce the effort and save time of measurements. For the purpose of predicting the heavy-oil viscosity, this work applies a number of different models to the dataset that is currently available for correlating viscosity to input parameters. The Gradient Boosting Regression Tree (GBR), the Support Vector Machine (SVM), and the Stochastic Gradient Decent (SGD) are the models that have been utilized in this investigation, and the genetic algorithm (GA) has been applied in order to optimize the hyper-parameters of these models. The RMSE error rates for the completed versions of the GBR, SGD, and SVM models are, in descending order, 26.6, 29.8, and 30.5. For the purposes of this research, the GBR model was determined to be the most suitable option among those available for estimation of crude oil viscosity.