Quantitative characterization of fractures and holes in core rolling scan images based on the MFAPNet deep learning model

Abstract

The exploration and comprehensive assessment of fractured-vuggy reservoir information have perennially constituted focal points and challenges within the domain of oil and gas reservoir evaluation. The verification of geological phenomena, identification of various fracture and hole types, and the quantitative characterization thereof currently present pressing challenges. This study meticulously examines the deep carbonate reservoirs within the Dengying Formation in the Penglai gas region of the Sichuan Basin. The Core Rolling Scan images reveal five discernible features: unfilled holes, filled holes, filled fractures, open fractures, and algae. The analysis pinpoints three primary challenges in semantic segmentation recognition: the amalgamation of feature scales, class imbalance, and the scarcity of datasets with substantial sample sizes. To address these challenges, this paper introduces a Multi-Scale Feature Aggregation Pyramid Network model (MFAPNet), achieving a pixel accuracy of 68.04% in recognizing the aforementioned five types. Lastly, the model is employed in calculating core porosity, exposing a scaling relationship between wellbore image porosity and core porosity ranging from 1.5 to 3 times. To a certain extent, it reveals the correlation between the wellbore image logging data and the actual formation of the Dengying Formation in the Penglai Gas Field of the Sichuan Basin, and also provides a basis for the subsequent logging evaluation of the formation. The partial code and CHA355 dataset are publicly available at https://github.com/zyng886/MFAPNet.