Performance investigation of Xanthan gum polymer flooding for enhanced oil recovery using machine learning models

Considering the importance of oil as a major global energy source and the decreasing discovery of new reservoirs, enhanced oil recovery (EOR) methods have attracted considerable attention to maximize production from existing fields. Among these methods, polymer flooding, especially using Xanthan gum, is highly effective. Xanthan gum was chosen as the focus of this study due to its wide availability, low cost and environmental compatibility which make it one of the most practical biopolymer candidates for EOR applications. However, EOR processes are sensitive and require extensive investigations, while field and laboratory experiments are often costly and time-consuming. This study introduces a machine learning-based model which enables petroleum engineers to quickly assess whether Xanthan gum polymer flooding is feasible for a given reservoir, eliminating the need for extensive experiments. Ten parameters API gravity, initial oil saturation (%), polymer concentration (ppm), porosity (%), salinity (wt%), rock type, pore volume flooding, permeability (md), oil viscosity (cp.), and temperature (°C) were used as inputs to five machine learning models, including neural networks such as multilayer perceptron (MLP), convolutional neural network (CNN), radial basis function (RBF), gated recurrent units (GRUs), and support vector regression (SVR). Among these, MLP (R² = 0.9930) and GRUs (R² = 0.9933) achieved the best predictions. These models provide a fast and cost-effective tool for petroleum engineers to make timely decisions regarding the use of Xanthan gum in EOR operations.