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

Unconventional Resources Pub Date : 2025-02-01 DOI:10.1016/j.uncres.2025.100147

Tao Liu , Zongbao Liu , Kejia Zhang , Feng Tian , Yan Zhang , Ruixue Zhang , Cuiyun Xu , Fang Liu , Xiaowen Liu , Haoran Wang , Mengning Mu

{"title":"Method for calculating porosity in tight sandstone reservoir thin sections based on ICSO intelligent algorithm","authors":"Tao Liu , Zongbao Liu , Kejia Zhang , Feng Tian , Yan Zhang , Ruixue Zhang , Cuiyun Xu , Fang Liu , Xiaowen Liu , Haoran Wang , Mengning Mu","doi":"10.1016/j.uncres.2025.100147","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101263,"journal":{"name":"Unconventional Resources","volume":"6 ","pages":"Article 100147"},"PeriodicalIF":0.0000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Unconventional Resources","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666519025000135","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

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.

Abstract Image