Investigation on hydrocarbon generation and expulsion potential by deep learning and comprehensive evaluation method: A case study of Hangjinqi area, Ordos Basin.

The Hangjinqi area is a significant gas exploration area from the northern Ordos Basin, an important petroliferous basin in China. Although a considerable amount of achievements has been made in the natural gas exploration field, the controversy about the hydrocarbon generation characteristics of source rocks has raged unabated for ten years. In addition, few studies have systematically investigated hydrocarbon expulsion potential in the Hangjinqi area. In this paper, we adopted a series of new approaches to assess the hydrocarbon generation and expulsion potential of source rocks in Hangjinqi. Specifically, we analyzed the rock pyrolysis, kerogen composition, and biomarker data and applied Convolution Neural Network (CNN) in the field of deep learning to determine organic matter abundance, type, maturity and depositional environment. Then, two techniques, the hydrocarbon generation kinetics method and the hydrocarbon generation potential method, were implemented to obtain both the hydrocarbon generation and expulsion quantity. The results reveal that the new technique CNN is suitable for TOC prediction, and the performance is improved largely compared to traditional methods, e.g., Back Propagation neural network (BP) and △log R. Rock pyrolysis data, kerogen macerals and biomarker data reveal that the source rocks are gas-prone and dominated by type Ⅲ. By the integrating hydrocarbon generation potential method and hydrocarbon generation kinetics method, the hydrocarbon generation quantity of the study area is 20.35 × 1012m3. The results of the hydrocarbon potential method indicate that about 23.7 × 108t and 0.74 × 108t hydrocarbons were expulsed from coal and mudstone, respectively. According to comprehensive analysis, the hydrocarbon generation and expulsion quantities in the Hangjinqi area are extensive, indicating good prospects for natural gas exploration. These findings have significant implications for the understanding of natural gas resources potential and the distribution of favorable natural gas areas. • We propose Convolutional Neural Network (CNN) to calculate TOC content. • Two methods were used to assess the hydrocarbon generation and expulsion potential. • The debated issue of hydrocarbon generation potential in Hangjinqi was resolved. • Hydrocarbon expulsion quantity in Hangjinqi was calculated for the first time. • Comprehensive geochemical data in Hangjinqi area are provided. [ABSTRACT FROM AUTHOR]