摘要
断层解释是油气勘探开发的基础工作之一。近年来,深度学习凭借其强大的数据分析能力,为精细刻画断层空间展布提供了新的技术工具,其应用前提在于如何获取大量可靠的样本数据。与目前最流行的断层正演样本数据相比,实际资料的断层专家解释样本数据存在一定的主观性,通常仅关注目标区域,断层样本标签并不完备。改进后的不完备断层样本标签损失函数将断层分析聚焦于已解释区域,提高了断层专家解释样本数据的可用性;通过对断层专家解释结果的样本数据增强处理,扩充了有效样本数据量;设计构建了联合专家解释样本及正演样本的三维断层自动识别网络结构,并在其中引入自注意力机制,提升了三维断层自动识别网络模型的泛化能力和空间特征分析能力。模型试验及实际应用测试结果表明,三维断层自动识别方法可融入实际断层发育特征,识别结果更符合地质认识,且精度也得到有效提升,从而验证了其可靠性及实用性。
Fault interpretation is one of the basic works of oil&gas exploration and development.In recent years,with its powerful data analysis ability,deep learning has provided a new technical tool for characterizing the spatial distribution of faults in detail.Its application relies on how to obtain a large number of reliable sample data.Compared with the most popu⁃lar fault data from forward modeling samples at present,fault data from expert interpretation samples of actual data are not only subjective but also focus on target areas,with incomplete labels of fault samples.The improved loss function of incom⁃plete labels of fault samples emphasizes interpreted areas in fault analysis,which improves the availability of fault data from expert interpretation samples.Through the enhancement processing of data from expert fault interpretation samples,the amount of effective sample data is increased.In addition,this paper designs and constructs a network structure of an au⁃tomatic 3D fault identification method driven by data from expert interpretation samples and forward modeling samples,and it introduces a self-attention mechanism to improve the generalization ability and spatial feature analysis ability of the auto⁃matic 3D fault identification network model.Model tests and practical application show that the proposed automatic 3D fault identification method can analyze actual features of the fault development.The identification results are more in line with geological conditions and the accuracy is effectively improved,which verifies the reliability and practicality of this method.
作者
于会臻
YU Huizhen(Exploration and Development Research Institute,Shengli Oilfield Company,SINOPEC,Dongying City,Shandong Province,257015,China)
出处
《油气地质与采收率》
CAS
CSCD
北大核心
2022年第6期58-66,共9页
Petroleum Geology and Recovery Efficiency
基金
中国石化科技攻关项目“基于大数据技术的油藏精细表征方法研究”(P20071-1)。
关键词
三维断层识别
深度学习
不完备断层样本
样本增强
地震断层解释
3D fault identification
deep learning
incomplete fault sample
sample enhancement
seismic fault interpretation
