Generation of expertly-interpreted models for prediction of core permeability

This article is devoted to prediction of core permeability. Permeability is one of the main properties for estimation of filtration of gas and liquid in core. To build a permeability model, porosity, density, depth of measurement, and other core physical properties are used. An algorithm for choosing the optimal prediction model is proposed. The model of superpositions of expertly-defined functions is suggested. The proposed method is a superposition of previously obtained optimal expetly-defined functions and a two-layer neural network. The experiment on core analysis, aero- and hydrodynamics datasets was conducted. During the experiment, the optimal expertly-interpreted models for all datasets were derived. The suggested approach is compared to other methods for choosing models, such as Lasso regression, support vector regression (SVR), gradient boosting, and neural network. The error and optimal parameters estimation was conducted using cross-validation. The experiment showed that the proposed approach is competitive with other state-of-the-art methods. Moreover, the number of neurons is significantly reduced with the use of superpositions of expertly-defined functions.