Log data-driven model and feature ranking for water saturation prediction using machine learning approach

Log-based reservoir characterization is one of the widely used techniques to estimate the reservoir properties and make decisions for hydrocarbon production. Use of the machine learning tools is becoming a more accessible approach for data-driven model development. The objective of this research is to identify and rank the most contributing log variables for estimation of water saturation using the machine learning tools. The multilayer perception artificial neural network (MLP-ANN) and kernel function-based least-squares support vector machine (LS-SVM) techniques are employed to develop predictive models for water saturation. The model can capture the non-linear behavior and high-dimensional complex relationships among real field log data variables. Based on the prediction performance of the models, the Levenberg-Marquardt algorithm-based MLP-ANN and the radial kernel…