This paper is the result of oil and gas exploration engineering.

Objective Source rocks are the fundamental material basis for hydrocarbon accumulation. However, due to the limited number of drilling core samples, geochemical analyses alone are often insufficient to provide a systematic evaluation of source rock characteristics throughout the entire well interval. Therefore, utilizing well-log data combined with artificial intelligence to conduct continuous quantitative evaluation of total organic carbon (TOC) in source rocks and source rock prediction is of great significance for a comprehensive and in-depth analysis of hydrocarbon accumulation conditions.

Methods This paper comprehensively investigates the geological characteristics of source rocks, as well as logging-based evaluation and intelligent prediction methods for total organic carbon (TOC), using geochemical analysis data of rock samples and logging data from key wells in the Permian Pusige Formation of the Southwestern Tarim Basin.

Results The results indicate that source rocks of the Pusige Formation are mainly composed of dark mudstones, with TOC values ranging from 0.01% to 20.6% (average 1.13%). The hydrocarbon generation potential (Pg) varies from 0.02 mg/g to 23.31 mg/g (average 2.76 mg/g). The kerogen types are predominantly Type Ⅱ₂ and Type Ⅲ, while vitrinite reflectance (R_o) values are mainly distributed between 0.55% to 2.55%, indicating a mature stage of thermal evolution. Overall, the source rocks are classified as fair to good source rocks. Due to the influence of argillaceous limestones and other lithologies with abnormally high resistivity, the conventional ΔlgR method exhibits poor applicability. To overcome this limitation, this study employed gamma ray (GR), deep resistivity (M2Rx), compensated neutron porosity (CNC), and compensated density (DEN) logging curves to establish a quantitative TOC prediction model using multiple linear regression and random forest machine learning methods. The random forest model performed significantly better than the multiple regression model, enabling continuous quantitative evaluation of source rock TOC in single-wells of the study area.

Conclusions This study enriches and deepens the geological understanding of the Permian Pusige Formation source rocks in the Southwestern Tarim Basin, establishes a TOC well log-based quantitative evaluation method for the study area, and provides a comprehensive evaluation of hydrocarbon generation potential, source–reservoir–seal assemblages, and exploration potential. The results indicate that the study area possesses favorable material foundations and hydrocarbon accumulation assemblages for the formation of large-scale hydrocarbon accumulations. The results offer important guidance for evaluating the Permian hydrocarbon resources in the region.