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[1]刘焱鑫1,黄继新2,尹艳树,等.针对油砂储层的岩心图像识别算法优选与应用[J].断块油气田,2020,27(04):464-468.[doi:10.6056/dkyqt202004011]
 LIU Yanxin,HUANG Jixin,YIN Yanshu,et al.Optimization and application of core image recognition algorithm in oil-sand reservoir[J].Fault-Block Oil and Gas Field,2020,27(04):464-468.[doi:10.6056/dkyqt202004011]
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针对油砂储层的岩心图像识别算法优选与应用(PDF)
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《断块油气田》[ISSN:1005-8907/CN:41-1219/TE]

卷:
27
期数:
2020年04
页码:
464-468
栏目:
默认栏目
出版日期:
2020-07-25

文章信息/Info

Title:
Optimization and application of core image recognition algorithm in oil-sand reservoir
作者:
刘焱鑫1黄继新2尹艳树1吕一兵3王超3齐建强4
1.长江大学地球科学学院,湖北 武汉 430100;2.中国石油勘探开发研究院,北京 100083;3.长江大学信息与数学学院,湖北 荆州 434023;4.中国石化中原油田分公司勘探开发研究院,河南 濮阳 457001
Author(s):
LIU Yanxin1 HUANG Jixin2 YIN Yanshu1 LYV Yibing3 WANG Chao3 QI Jianqiang4
1.School of Geoscience, Yangtze University, Wuhan 430100, China; 2.Research Institute of Petroleum Exploitation and Development,PetroChina, Beijing 100083, China; 3.School of Information and Mathematics, Yangtze University, Jingzhou 434023, China; 4.Exploration and Development Research Institute, Zhongyuan Oilfield Company, SINOPEC, Puyang 457001, China
关键词:
岩心图像自动识别图像分割算法软件开发加拿大油砂
Keywords:
automatic core image recognition image segmentation algorithm software development Canadian oil sands
分类号:
-
DOI:
10.6056/dkyqt202004011
文献标志码:
A
摘要:
在地质研究及石油生产开发过程中,岩心资料的识别与表征具有重要意义。加拿大麦凯河油砂区块下白垩统麦克默里组储层岩心中发育数量众多的毫米级泥质夹层,人工识别工作量大,亟需采用图像识别算法自动识别。文中选择OTSU分割、粒子群双阈值分割、FCM聚类分割及神经网络方法,开展针对薄夹层自动识别对比研究。岩心识别结果表明,粒子群算法识别平均准确度达到90.29%,识别准确度最高,识别速度快、可靠性高且易于实现,能够完成大规模的岩心薄夹层识别工作。基于此,开发了一套薄夹层识别软件,服务于研究区及相似区块薄夹层的识别,为油藏勘探开发及地学数字化提供技术支持。
Abstract:
Recognition and characterization of core data are of important significance to geological studies and exploitation of petroleum. The reservoir core of Lower Cretaceous McMurray Formation of McMurray River oil-sand block in Canada is developed with abundant millimeter-scaled muddy interlayers. Therefore, the artificial recognition workload is heavy and automatic recognition based on the image recognition algorithm is needed urgently. In this study, various methods such as OTSU segmentation, particle swarm double-threshold segmentation, FCM clustering segmentation and neural network are selected to carry out the contrastive research on automatic recognition of thin interlayers. The core recognition results show that the average recognition accuracy of particle swarm optimization(PSO) algorithm is the highest, reaching 90.29%. Moreover, PSO algorithm is characterized by high recognition speed, high reliability and feasibility, and can recognize large-scaled core interlayers. Based on PSO algorithm, a set of thin interlayer recognition software is developed for recognition of thin interlayers in the study area and similar blocks. It provides strong supports for oil reservoir exploration and exploitation and the promotion of geoscience digitalization.

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更新日期/Last Update: 2020-07-24