基于深度学习的地震断层检测与断面组合  

Deep learning-based seismic fault detection and surface combination

作　　者：王子健 伍新明 杜玉山[2] 张强 于会臻[2] WANG Zijian;WU Xinming;DU Yushan;ZHANG Qiang;YU Huizhen(School of Earth and Space Sciences,University of Science and Technology of China,Hefei City,Anhui Province,230026,China;Exploration and Development Research Institute,Shengli Oilfield Company,SINOPEC,Dongying City,Shandong Province,257015,China)

机构地区：[1]中国科学技术大学地球和空间科学学院,安徽合肥230026 [2]中国石化胜利油田分公司勘探开发研究院,山东东营257015

出　　处：《油气地质与采收率》2022年第1期69-79,共11页Petroleum Geology and Recovery Efficiency

摘　　要：断层解释是油气勘探和开发中的关键步骤,由于采集的三维地震数据体数量增多,人工以及传统方法很难精细化解释数据体中的断层。为了更好地满足目前油气勘探开发对高效、高精度、高分辨率断层解释的迫切需求,研究基于深度学习算法实现地震数据的自动化和智能化断层检测。通过正演模拟的方法生成大量的、多样化的、符合实际情况的训练数据,同时结合已解释的断层结果构建完备的训练样本库。在此基础上设计优化的、简单的三维卷积神经网络模型高效处理大的三维地震数据体并获得精确的断层检测结果,对断层检测结果做进一步的匹配滤波扫描处理来获得增强的断层概率体、断层倾向和走向估计。最后根据这3个断层属性体,采用区域生长算法来全自动构建出数据体中所有的断层面。通过与传统的常规方法进行对比,该方法在抗噪性、精度和效率等方面均具备明显的优势。The fault interpretation is a crucial step in oil and gas exploration and development.Due to the increase in the number of collected 3D seismic data volumes,the manual and traditional methods can hardly interpret faults in data volumes in detail.To better meet the urgent needs for high-efficiency,high-precision,and high-resolution fault interpretation in oil and gas exploration and development,we propose a deep learning-based algorithm to realize the automatic and intelligent fault detection with seismic data.The forward modeling method is used to generate a large number of diversified training data in line with the actual situation,and at the same time,a complete training sample volume is constructed in combination with the existing fault interpretation results.On this basis,an optimized and simple three-dimensional(3D)convolutional neural network(CNN)model is designed to efficiently process large 3D seismic data volumes and obtain accurate fault detection results.We further apply scan processing of matched filtering to the fault detection results to enhance the fault probability volumes and at the same time,obtain an estimation of fault strikes and dips.Given the three fault attribute volumes,we finally utilize a region-growing algorithm to automatically construct all the fault surfaces in the data volumes.Compared with the conventional methods commonly used in the industry,our method is significantly superior to the conventional methods in robustness to noise,accuracy,and efficiency.

关 键 词：断层解释 深度学习 正演模拟 卷积神经网络 断面组合 

分 类 号：TE319[石油与天然气工程—油气田开发工程]