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

1.成都理工大学地球物理学院,四川成都 610059;
2.成都理工大学地球勘探与信息技术教育部重点实验室,四川成都 610059

1. College of Geophysics,Chengdu University of Technology,Chengdu 610059,China;
2. Key Laboratory of Earth Exploration and Information Technology of Ministry of Education (Chengdu University of Technology),Chengdu 610059,China

碳酸盐岩储层中的裂隙是油气的运移通道和储集空间,对于油气勘探、开发和评价都具有重要的指导意义。针对研究区碳酸盐岩储层裂缝检测的难题,提出灰狼布谷鸟优化BP算法(GWO-CS-BP),该算法是将GWO-CS(grey wolf-cuckoo search algorithm)与BP(back propagation)相结合形成的裂隙检测方法。将含裂缝信息的相干、曲率、倾角、方位角和构型张量等属性作为GWO-CS-BP神经网络的输入数据,在工区地质资料约束下根据测井数据获得裂缝发育水平评价指标,进而对研究区裂缝发育水平进行评价并划分等级。研究区碳酸盐岩储层裂缝发育水平检测结果表明,GWO-CS-BP算法能够综合各属性特点对研究区的裂缝发育水平特征进行二次误差控制,获得裂缝发育水平评价指标f_s并将研究区裂缝发育水平划分为3个等级及4个裂缝存在区域。其中,当研究区裂缝发育水平参数的值适中时,即f_s的值大于4.0且小于5.8时,C区域最有利于油气的聚集,高产井的分布数量较多。利用GWO-CS-BP算法对研究区的裂缝发育水平进行了精细评价,并得出裂隙发育水平参数f_s,实现了GWO-CS算法改进的BP神经网络在裂缝检测中的有效应用。

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 obtained 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 f_s obtained,fractures in the study area could be classified into three grades in four zones.For the f_s 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 evaluation index f_s,the modified BP neural network by a GWO-CS algorithm yields a detailed evaluation of fractures in the study area.

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。

10.12431/issn.1000-1441.2024.63.04.012