基于深度学习的露头地层剖面裂缝自动提取

Siqi Wu, Qing Wang, Qihong Zeng, Youyan Zhang, Yuangang Liu, Yanlin Shao, Wei Wei, Fan Deng, Changmin Zhang

科研成果: 期刊稿件文章同行评审

摘要

At present, the main targets of oil and gas exploration and development in China are gradually shifting to carbonate oil and gas resources and shale oil and gas resources, and carbonate reservoirs are mainly fracture-vuggy reservoirs. The extraction of fractures from outcrop formation profiles can provide intuitive and vivid results for the study of fracture-vuggy reservoirs. The most common traditional research on outcrop fractures is time-consuming and labor-intensive manual interpretation.Therefore, an intelligent fracture extraction technology based on deep learning is proposed.Based on the Mask R-CNN algorithm, the online augmentation strategy and attention mechanism are combined to realize automatic fracture extraction.In order to prove the error tolerance ability of the improved Mask R-CNN algorithm proposed in this study, comparison experiments were designed for training sets containing different degrees of error labels.The results showed that the accuracy of the model decreased greatly when the proportion of error labels reached 30%.Then, through the quantitative analysis of ablation experiments, different improvements can improve the accuracy of the algorithm. The results show that the accuracy of the model is improved after the introduction of attention mechanism and online enhancement.Then, the accuracy of manual extraction was compared with OTSU method, regional growth algorithm, UNet algorithm and DeepLabv3+ algorithm.The results show that the linear features extracted by the proposed method are more accurate and complete, and the accuracy is more than 97%, which is higher than other comparison algorithms, and verifies the effectiveness of the improved Mask R-CNN method.Finally, the method was applied to extract fractures from outcrop profiles in the Ebian Xianfeng area, southwest Sichuan Basin. The parameters of fracture length, density, inclination and spacing were analyzed statistically, and the fracture distribution characteristics were quantitatively analyzed.The feasibility of the improved Mask R-CNN method was verified. This method provides a basis for speeding up the comprehensive study of petroleum exploration and target evaluation, reservoir prediction and automatic development of outcrop profile parameter characterization.

投稿的翻译标题Automatic fracture extraction from outcrops using deep learning
源语言繁体中文
页(从-至)245-257
页数13
期刊Geophysical Prospecting for Petroleum
62
2
DOI
出版状态已出版 - 25 3月 2023
已对外发布

关键词

  • automatic fracture extraction
  • convolutional neural network
  • deep learning
  • fracture parameter characterization
  • outcrop formation profile

指纹

探究 '基于深度学习的露头地层剖面裂缝自动提取' 的科研主题。它们共同构成独一无二的指纹。

引用此