Prediction of permeability reduction by external particle invasion using Artificial Neural Networks and Fuzzy Models

The transport of fine particles is one of the major causes of permeability reduction in porous media. A number of mathematical models have been suggested in the literature to simulate and quantify this reduction. Simple models include analytical solutions of the equations that describe the phenomenon, while more complex models are solved by numerical methods. In this study, an Artificial Neural Network (ANN) and a Fuzzy Model (FM) were developed to predict the permeability reduction by external particle invasion in non-consolidated porous media. For the training process, the results of 42 laboratory experiments were employed. The input data covered a wide range of porosity, permeability, injection rates, and fines concentrations. The developed FM and ANN were tested with eight sets of experiments that were not used in the training. The results show that the ANN can match and predict, with high precision, the permeability reduction as a function of pore volumes of fine suspensions injected. The FM predicts the permeability reduction with moderate precision.