Predicting the thermal maturity of source rock from well logs and seismic data in basins with low-degree exploration

Vitrinite reflectance (R-o) is a significant geological index that evaluates the thermal maturity of source rock. However, measured R-o data and suitable evaluation methods are lacking, making it difficult to predict the thermal maturity of source rock in basin, especially in those with a low exploration degree. In this study, we propose a novel method for estimating R-o from well logs and seismic data using seismic velocity inversion and the vitrinite reflectance-mudstone porosity (R-o-phi) model. First, using a wavelet neural network, acoustic transit time curve was reconstructed from spontaneous potential, gamma rays, resistivity, and density logs, and new curve was used as inputs in a colored inversion to obtain seismic relative velocity. Second, a low-frequency model was established to counteract critical frequency and sequence framework constraints. Third, the seismic absolute velocity was merged by relative and low-frequency velocity components. Finally, R-o distributions were calculated using the R-o-phi model. Our findings indicate that the inversion effect improved with low-frequency supplement; compared with the graphic and geochemical method, the R-o-phi model predicted R-o in poor-data areas more accurately, with a relative error of <9.5205%. We also propose an explanation for the appearance of highly mature source rocks distributed from dispersion to aggregation in depression-rift transitions: the thick stratum in the downthrow wall of boundary faults promote source rock maturity in the rift period; hence, in an open depression, the source rocks in large subsidence areas are highly mature, particularly near the downthrow wall of a boundary fault, which has the highest maturity owing to the superimposed subsidence of the overlying rifted basin.