Method for calculating porosity in tight sandstone reservoir thin sections based on ICSO intelligent algorithm

作者：Tao Liu, Zongbao Liu, Kejia Zhang, Feng Tian, Yan Zhang, Ruixue Zhang, Changjun Xu, Fang Liu, Xiaowen Liu, Haoran Wang, Mengning Mu · 发表于：Unconventional Resources · 年份：2025 · DOI：10.1016/j.uncres.2025.100147 · 被引用次数：1 · 研究领域：Drilling and Well Engineering、Hydrocarbon exploration and reservoir analysis、Reservoir Engineering and Simulation Methods

Surface porosity is crucial for evaluating tight sandstone reservoirs' performance and resource potential. The current manual calculation and algorithm extraction methods have problems such as heavy workload, long time consumption, low accuracy in identifying complex pore morphologies, and weak learning ability for sparse samples. Drawing on the concept of hybrid intelligence, this paper proposes an intelligent calculation method for the surface porosity of tight sandstone reservoirs (ICSO) that combines the SOLOv2 algorithm and OpenCV. The SOLOv2 instance segmentation algorithm was used to segment and label pore regions in images. OpenCV was employed to extract pore distribution and proportions, thereby realizing the calculation of surface porosity. The performance comparison with similar algorithms demonstrates the advantages of this method in terms of accuracy, running speed, and generalization ability. It addresses the surface porosity calculation issue and provides a novel research approach for solving similar problems in related fields. The Graphical Abstract consists of three parts: establishment of a thin section dataset, identification of pore components, and calculation of thin section porosity. ICSO involves establishing a cast thin section image dataset through preprocessing, applying the instance segmentation SOLOv2 algorithm to identify pore regions in the images, and utilizing OpenCV to extract pore distribution and proportion for thin section porosity calculat...