Permeability prediction of porous media using a combination of computational fluid dynamics and hybrid machine learning methods

Permeability prediction is crucial in shale gas and CO2 geological sequestration. However, the intricate pore structure complicates the prediction of permeability. Machine learning (ML) is a promising approach for predicting inherent correlations in large data sets. In this paper, a hybrid ML method is proposed to implicitly build a nonlinear relationship between pore structure parameters and permeability. For the dataset preparation, an improved quartet structure generation set algorithm was firstly developed to generate 1000 porous media. Then, the pore structure parameters were extracted as input parameters and the permeability was calculated as the output parameter. For the ML modelling, a hybrid ML method was proposed using a combination of artificial neural network (ANN) and genetic algorithm (GA). The ANN was employed to learn the nonlinear relationships and GA was used to tune ANN architecture for the best performance. The prediction results show that the GA–ANN was robust in predicting permeability based on pore structure parameters. The ANN model with the optimum architecture could achieve an average R value of 0.998 on the training set and 0.999 on the testing set. Practically, the porous sample can be obtained through micro-computed tomography (CT) or nano-CT, and the proposed framework can be applied to real porous media. Fast prediction of permeability based on formation factors can provide some insights on reservoir evaluation and reservoir stimulation.