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

• Published in: Journal of applied geophysics 2024-02, Vol.221, p.105300, Article 105300
• Main Authors: Li, Chenxi, Liu, Zhen, Chen, Chen, Wang, Yonggang, Liu, Feng, Xu, Min, Yang, Yi, Wang, Biao, Chen, Shuguang
• Format: Article
• Language: English
• Subjects: Colored inversion; Linhe Depression; Mudstone porosity; Seismic velocity; Vitrinite reflectance; Wavelet neural network
• ISSN: 0926-9851; 1879-1859
• DOI: 10.1016/j.jappgeo.2024.105300

Vitrinite reflectance (Ro) is a significant geological index that evaluates the thermal maturity of source rock. However, measured Ro 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 Ro from well logs and seismic data using seismic velocity inversion and the vitrinite reflectance-mudstone porosity (Ro-ϕ) 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, Ro distributions were calculated using the Ro-ϕ model. Our findings indicate that the inversion effect improved with low-frequency supplement; compared with the graphic and geochemical method, the Ro-ϕ model predicted Ro in poor-data areas more accurately, with a relative error of