Journal of Petroleum Engineering & Technology Original Research
AI-Driven Prediction of Polymer Dynamic Adsorption in Porous Media for Enhanced Oil Recovery Applications
Abstract
Polymer flooding stands as one of the most commercially mature chemical enhanced oil recovery (EOR) techniques utilized to improve sweep efficiency in mature reservoirs. However, the economic and operational viability of polymer flooding is heavily constrained by polymer retention, primarily driven by dynamic adsorption onto the rock surface within porous media. This study presents an integrated framework combining rigorous core flooding experiments with advanced machine learning algorithms to predict dynamic polymer adsorption under diverse reservoir conditions. Using partially hydrolyzed polyacrylamide (HPAM) solutions, dynamic core flooding experiments were conducted across variations in temperature (30°C to 90°C), brine salinity (5,000 to 50,000 ppm), rock permeability (50 to 500 mD), and injection flow rates. The experimental dataset was utilized to train, validate, and compare three machine learning architectures: Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), and Random Forest.The XGBoost model exhibited superior predictive capabilities, capturing non-linear interactions between the reservoir variables and dynamic adsorption behavior with minimal error. Feature importance analysis revealed that brine salinity and rock permeability are the most critical governing factors influencing polymer retention. By accurately forecasting dynamic adsorption and subsequent permeability reduction, this AI-driven approach significantly reduces the reliance on time-consuming, destructive core flooding procedures. This research offers an predictive tool for optimizing chemical injection strategies, reducing polymer wastage, and accelerating digital oilfield integration for smart reservoir management.
Keywords
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