Analyzing key controlling factors of shale reservoir heterogeneity in "thin" stratigraphic settings: A deep learning-aided case study of the Wufeng-Longmaxi Formations, Fuyan Syncline, Northern Guizhou

Abstract

The Wufeng-Longmaxi Formation shales are key targets for shale gas exploration, but they are often studied as a single stratigraphic unit with limited analysis of internal differences. This study combines traditional geological methods with deep learning to compare the reservoir characteristics of the Wufeng Formation, the first member of the Longmaxi Formation (Long 1), and the second member of the Longmaxi Formation (Long 2), identifying the main controlling factors of differences. We found that: (1) The Wufeng Formation primarily develops siliceous shale lithofacies (S), mixed siliceous shale lithofacies (S-2), and clay siliceous shale lithofacies (S-3). Long 1 develops mixed siliceous shale lithofacies (S-2) and clay siliceous shale lithofacies (S-3), while Long 2 exhibits clay and siliceous mixed shale lithofacies (M-2) and siliceous clay shale lithofacies (CM-1). (2) The YOLO-v8 model demonstrates higher accuracy in shale pore type detection than the YOLO-v10 model, with a maximum mAP of 78.9 %. Using the YOLO-v8 model, it was found that S, S-2, and S-3 lithofacies are dominated by dissolution pores and organic pores with larger specific surface areas and porosities, while CM-1 and M-2 lithofacies are characterized by dissolution pores with smaller specific surface areas and porosities. (3) Based on evaluation indicators such as TOC content, BET surface area, porosity, brittleness index, and gas content, S and S-2 are classified as Class I lithofacies, S-3 as Class II lithofacies, and M-2 and CM-1 as Class III lithofacies. The main controlling factor for the heterogeneity of the shale reservoirs in the study area is lithofacies.