Intelligent Fast Calculation of Petrophysical Parameters of Clay-Bearing Shales Based on a Novel Dielectric Dispersion Model and Machine Learning

Dielectric dispersion and its interpretation process through clay-bearing shales is very complicated, which makes the saturation evaluation of clay-bearing shales difficult. This paper focuses on developing a model that considers the clay effect on the dielectric dispersion of clay-bearing shales. The effects of water saturation, clay content, and other factors on the dielectric dispersion characteristics of clay-bearing shale rocks are analyzed. By combining a dielectric dispersion response database with backpropagation neural network (BPNN) models, this paper develops a calculation model that can simultaneously calculate five petrophysical parameters, i.e., the water salinity, rock cementation exponent, clay content, clay moisture content, and water saturation. The results indicate that the newly developed dielectric dispersion model can characterize the effects of clay content and clay moisture content. The correlation coefficients of the five parameters can all exceed 99% for each sub-sample database and reach an average of 95.06% in an application case, and the calculation efficiency is also very satisfactory, which significantly outperforms the traditional optimization algorithms. The proposed method provides a practical alternative to traditional inversion approaches for shale evaluation.