石油物探2024，Vol.63Issue(4)：833-845,13.DOI:10.12431/issn.1000-1441.2024.63.04.012
用于碳酸盐岩储层裂缝检测的GWO-CS-BP算法及应用研究
GWO-CS-BP algorithm and its application to fracture detection in carbonate reservoirs
摘要
Abstract
Fractures in carbonate reservoirs are the migration channels and reservoir space of oil and gas,and fracture prediction has important guiding significance for oil and gas exploration,development and evaluation.A GWO-CS-BP algorithm is proposed to solve the problem of fracture detection in carbonate reservoirs in the study area.The algorithm combines GWO-CS(grey wolf-cuckoo search)and BP(back propagation).Coherence,curvature,dip angle,azimuth angle and configuration tensor are used as the input data of a GWO-CS-BP neural network,which is constrained by logging and geological data.An evaluation index is thereby ob-tained to evaluate and grade fractures in the study area.The detection results show that the GWO-CS-BP algorithm can integrate the characteristics of each attribute for secondary error control on fracture detection.As per the evaluation index fs obtained,frac-tures in the study area could be classified into three grades in four zones.For the fs lies in the range of 4.0 to 5.8,which indicates a moderate degree of development,area C with many high-yield wells is most conducive to oil and gas accumulation.Based on the e-valuation index fs,the modified BP neural network by a GWO-CS algorithm yields a detailed evaluation of fractures in the study area.关键词
地震属性/裂缝检测/GWO-CS优化算法/BP神经网络/碳酸盐岩储层Key words
seismic attribute/fracture detection/optimized GWO-CS algorithm/BP neural network/carbonate reservoir分类
天文与地球科学引用本文复制引用
李琼,张宇,石林坤..用于碳酸盐岩储层裂缝检测的GWO-CS-BP算法及应用研究[J].石油物探,2024,63(4):833-845,13.基金项目
国家科技重大专项(2016ZX05026001-004)和四川省重点研发计划(2020YFG0157)共同资助.This research is financially supported by the National Science and Technology Major Project(Grant No.2016ZX05026001-004),the Key Research and Development Program of Sichuan Province(Grant No.2020YFG0157). （2016ZX05026001-004）
