摘要
面孔率是评估致密储层质量和资源潜力的关键指标。目前基于单一图像分析技术的储层孔隙智能提取及面孔率计算方法存在前期配置繁琐、稀疏样本学习能力弱、复杂孔隙形态识别准确率不高等问题。为此,本文基于混合智能思想,提出一种融合SOLO(segmenting objects by locations)v2算法和OpenCV(open source computer vision library)的致密砂岩薄片面孔率智能计算方法。使用实例分割算法SOLOv2分割和标记图像中的孔隙区域,结合OpenCV提取孔隙分布和占比,进而计算面孔率。对比实验结果表明,该方法在Dice系数(0.88)、像素准确率(0.91)和面孔率计算误差(<0.1)方面优于对比算法YOLACT(you only look at coefficients)、Mask R-CNN(mask region-based convolutional neural network)和SOLO,且执行速度更快。
Plane porosity is a key indicator for assessing the quality and resource potential of tight reservoirs.The current reservoir pore intelligent extraction and plane porosity calculation methods based on single image analysis technology have problems such as cum bersome pre-configuration,weak learning ability of sparse samples,and low accuracy of complex pore morphology recognition.For this reason,this paper proposes an intelligent calculation method of plane porosity in tight sandstone thin section by integrating SOLO(segmenting objects by locations)v2 algorithm and OpenCV(open source computer vision library)based on the idea of hybrid intelligence.Using the instance segmentation algorithm SOLOv2 to segment and label the pore regions in the image,and the distribution and percentage of pores are extracted in combination with OpenCV,then the plane porosity is calculated.Comparative experimental results show that this method is superior to the comparative algorithms such as YOLACT(you only look at coefficients),Mask R-CNN(mask region-based convolutional neural network)and SOLO in terms of Dice coefficient(0.88),pixel accuracy(0.91),and plane porosity calculation error(<0.1),with a faster execution speed.
作者
张可佳
徐意行
刘宗堡
田枫
赵玉武
刘涛
张岩
贺友志
Zhang Kejia;Xu Yixing;Liu Zongbao;Tian Feng;Zhao Yuwu;Liu Tao;Zhang Yan;He Youzhi(School of Com puter&Information Technology,Northeast Petroleum University,Daqing 163318,Heilongjiang,China;Heilongjiang Key Laboratory of Petroleum Big Data and Intelligent Analysis,Daqing 163318,Heilongjiang,China;School of Earth Sciences,Northeast Petroleum University,Daqing 163318,Heilongjiang,China;No.8 Oil Production Plant of Daqing Oilfield Limited Com pany,PetroChina,Daqing 163514,Heilongjiang,China)
出处
《吉林大学学报(地球科学版)》
北大核心
2025年第5期1774-1784,共11页
Journal of Jilin University:Earth Science Edition
基金
国家自然科学基金项目(42172161)
黑龙江省教育厅人才类项目(UNPYSCT-2020144)
黑龙江省省属本科高校基本科研业务费项目(2022TSTD-03)
黑龙江省高校基本科研业务费项目(2022YDL-15)。
