A novel approach for pore-scale study of fines migration mechanism in porous media

Abstract

The migration of fine particles in oil reservoir rock is the most important cause of formation damage and permeability impairment. The mechanism of this phenomenon includes fines detachment, mobilization, agglomeration, and re-attachment, which lead to throat clogging and/or blocking. Therefore, accurate modeling of this complex mechanism requires a model with high solver efficiency.In this work, based on the physical features of fines migration in pore throat, the conventional DPM model of ANSYS FLUENT software was initially modified by UDF code. Then, predetermined scenarios of suspension injection into a two dimensional (2D) microchannel were simulated for predicting pressure drop as a function of injected pore volume using the modified DPM model. Next, based on these simulation results, a proxy model using the artificial neural network (ANN) was developed for computational cost reduction of fines migration modeling in the microchannel (pore throat). Secondly, a dual pore scale numerical model by combination of proxy model and pore network modeling approach was developed to predict pressure drop as well as permeability impairment in a network of microchannels (porous media). Moreover, for rapid computation of pressure drop in the network model, a meta-heuristic method of rain optimization algorithm (ROA) has been used. Finally, for model validation, a series of laboratory tests including suspension injections into the glass micromodels were conducted.A comparison of simulation results using both proxy model and dual pore scale model with experimental data shows a good agreement and according to the obtained results, a decrease in the particles mean size, and Reynolds number as well as increase in particles concentration lead to increase in pressure drop and permeability reduction. On the other hand, use of a proxy model based on artificial neural network and a meta-heuristic method of rain optimization algorithm lead to significant computational cost reduction, while the accuracy of fines migration modeling is high enough. Hence, using our modeling approach for fines migration study in porous media as an accurate and fast computational tool can help for a better understanding of this phenomenon, which is very important in the petroleum industry.