摘要
高精度的断层检测是油气勘探开发过程中的核心任务之一,是规避潜在的工程风险、确保钻井操作安全的重要手段。随着勘探规模的扩大,传统的人工断层解释和常规的断层检测方法难以满足实际需求。深度学习方法为地震断层智能识别提供了一种重要途径,其中以Unet为代表的深度网络模型在该类任务中取得了诸多成功的案例。然而,由于卷积运算的特殊性,该方法在特征提取过程中丢失了部分信息,导致断层识别的准确性和鲁棒性有待进一步提升。将CNN-Transformer混合模块嵌入Unet网络框架中,提出了一种基于U-CNNformer的混合网络模型。混合网络模型提高了对样本集全局特征与局部细节的挖掘能力,克服了传统Unet网络在断层识别中信息关联性不强的局限,在保证断层识别精度的同时,提高了模型的鲁棒性。北海F3公开数据测试和我国四川盆地某实际数据的应用表明,混合网络模型不仅能更精确地检测断层特征,对断层分布的刻画也更为细致,实现了高精度的断层智能识别,可为钻井高效、安全地开发提供良好的支撑。
Fault interpretation is one of the core tasks in oil and gas exploration and development.However,with the increase of exploration scale,traditional artificial fault interpretation and conventional fault detection methods are unable to meet practical needs.Deep learning methods provide an important approach for intelligent seismic fault recognition,among which deep network models represented by Unet have achieved many successful cases in this type of task.However,due to the particularity of convolution operations,this method loses some information in the feature extraction process,resulting in the need for further improvement in the accuracy and robustness of fault recognition.In this paper,we design a CNN-Transformer hybrid module and embed it into the Unet network framework,proposing a hybrid network model based on U-CNNformer.The hybrid network model improves the mining ability of both global features and local details in the sample set,overcomes the limitations of the conventional Unet network in weak information correlation in fault recognition,and improves the robustness of the model while ensuring the accuracy of fault recognition.Testing on the publicly available North Sea F3 data and applying with actual data in a certain area of Sichuan Basin in China demonstrate that the proposed hybrid network model not only accurately detects fault features but also provides a more detailed characterization of fault distribution,achieving high-precision intelligent fault recognition with excellent application effectiveness.
作者
安虹伊
文馨
李居正
张惊喆
张琳智
房平超
杜天玮
张奎
王群武
AN Hongyi;WEN Xin;LI Juzheng;ZHANG Jingzhe;ZHANG Linzhi;FANG Pingchao;DU Tianwei;ZHANG Kui;WANG Qunwu(Exploration Department,PetroChina Southwest Oil&Gasfield Company,Chengdu,Sichuan 610095,China;Kunlun Digital Technology Co.,Ltd,Beijing 102206,China;Beijing Precise Energy Technology Co.,Ltd)
出处
《海相油气地质》
北大核心
2025年第3期277-288,共12页
Marine Origin Petroleum Geology
基金
中国石油西南油气田项目《勘探事业部2024年探井随钻地震跟踪技术服务(北京普瑞斯安)》(编号:XNS勘探部YT2023-35)资助。
