This study addresses the critical challenge of excessive water production in mature oil and gas reservoirs. It focuses on the effectiveness of polymer gel injection into porous media as a solution, with an emphasis on understanding its impact at the pore scale. A step-wise Lattice Boltzmann Method (LBM) is employed to simulate polymer gel injection into a 2D Berea sample, representing a realistic porous media. The non-Newtonian, time-dependent characteristics of polymer gel fluid necessitate this detailed pore-scale analysis. Validation of the simulation results is conducted at each procedural step. The study reveals that the methodology is successful in predicting the effect of polymer gel on reducing permeability as the gel was mainly formed in relatively larger pores, as it is desirable for controlling water cut. Mathematical model presented in this study accurately predicts permeability reductions up to 100% (complete blockage). In addition, simulations conducted over a wide range of gelation parameters, TD_factor from 1 to 1.14 and Threshold between 0.55 and 0.95, revealed a quadratic relationship between permeability reduction and these parameters. The result of this research indicates LBM can be considered as promising tool for investigating time-dependant fluids on porous media.@en

Cross-linked polymer gel is widely used in the oil and gas industry to block high permeability conduits and reduce water cut. The complex nature of this fluid, especially regarding flow in porous media, makes its numerical simulation very time-consuming. This study presents an approach to designing an Artificial Neural Network (ANN) model that could predict the permeability reduction caused by injecting polymer gel into a 2D rock sample. Our methodology consists of two main parts: numerical simulation and ANN model building. Considering the advantages of the Lattice Boltzmann Method (LBM) this approach is used to model the injection of polymer gel in porous media. Using this model, more than 20,000 simulations were performed which resulted in highly unbalanced dataset, so an innovative approach for balancing regression dataset is also proposed in detail in this paper. The final constructed ANN model could predict the permeability reduction in a fraction of a second with less than 2.5% Mean Absolute Error (MAE). The result indicates the importance of balancing datasets to obtain a reliable prediction from ANN. Also, it should be mentioned that gelation parameters had the most significant impact on the value of permeability reduction, with mean absolute SHapley Additive exPlanations (SHAP) values of 20 and 12.5 for TDfactor and Threshold, respectively.

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