Support Vector Machine Model for Predicting Gas Saturated and Undersaturated Crude Oil Viscosity of Niger Delta Oil Reservoir
Isemin Isemin
Department of Petroleum Engineering, University of Ibadan, Ibadan, Nigeria and Chemical and Petroleum Engineering Department, University of Uyo, Uyo, Nigeria.
Akinsete Oluwatoyin *
Department of Petroleum Engineering, University of Ibadan, Ibadan, Nigeria.
Akpabio Julius
Chemical and Petroleum Engineering Department, University of Uyo, Uyo, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Oil viscosity is one of the most important physical and thermodynamic property used when considering reservoir simulation, production forecasting and enhanced oil recovery. Traditional experimental procedure is expensive and time consuming while correlations are replete however they are limited in precision, hence need for a new Machine Learning (ML) models to accurately quantify oil viscosity of Niger Delta crude oil.
This work presents use of ML model to predict gas-saturated and undersaturated oil viscosities. The ML used is the Support Vector Machine (SVM), it is applicable for linear and non-linear problems, the algorithm creates a hyperplane that separates data into two classes. The model was developed using data sets collected from the Niger Delta oil field. The data set was used to train, cross-validate, and test the models for reliability and accuracy. Correlation of Coefficient, Average Absolute Relative Error (AARE) and Root Mean Square Error (RMSE) were used to evaluate the developed model and compared with other correlations.
Result indicated that SVM model outperformed other empirical models revealing the accuracy and advantage SVM a ML technique over expensive empirical correlations.
Keywords: Saturated viscosity, undersaturated viscosity, support vector machine, crude oil