Machine learning in reservoir permeability prediction and modelling of fluid flow in porous media

Abstract

Reliable data on the properties of the porous medium are necessary for the correct description of the process of displacing hydrocarbons from the reservoirs and forecasting reservoir performance. The true permeability of the reservoir is one of the most important parameters which determination is time-consuming, costly and require skilled labor. The paper describes the methodology for determining the permeability of a porous medium, based on machine learning. The results of laboratory experiments, available in the database (terrigenous reservoirs with permeability in the range from 12 to 1132 md), are used to train the neural network, and then to predict the reservoir permeability. Comparison of the predicted and calculated permeability values showed a fairly good match between them with the determination coefficient of 0.92. The last task considered in this paper is to obtain an analytical expression describing a fluid flow in a porous medium using machine learning. This procedure enabled to obtain a resultant equation of fluid flow in a wide range of reservoir parameters and pressure gradients, which can be used in reservoir simulators.