The carbonate fault–karst reservoir is a special and significant reservoir in the Shunbei area, and the development of the cave has been controlled by strike-slip faults. Due to the complex subsurface structures, fault–karst reservoir characterization is generally divided into fault and cave detection tasks. The potential spatial relationships between faults and caves might be neglected by using the separate detection scheme. The multi-task learning network can perform multiple tasks simultaneously and exploit the potential features of training data by using a deep neural network. In this study, we built fault–karst models based on the geological background of the Shunbei area and synthesized fault–karst training data using three-dimensional point spread function convolution. Then, we developed a multi-task learning network to learn fault–karst features and detect faults and caves simultaneously. The test result demonstrates that the multi-task learning network trained by synthetic fault–karst data can effectively identify the faults and caves in field seismic data. The comparisons of the multi-task learning network, single-task learning networks and conventional methods demonstrate the importance of spatial relationships between faults and caves and show the superiority of the multi-task learning network. This technique could significantly assist in the exploration, development and well deployment for an ultra-deep carbonate reservoir.

中文翻译：

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通过多任务学习表征深层碳酸盐岩断岩溶储层

碳酸盐断岩溶储层是顺北地区特殊而重要的储层，洞穴的发育受走滑断裂的控制。由于地下结构复杂，断岩溶储层表征一般分为断层和洞穴检测任务。使用单独的检测方案可能会忽略断层和洞穴之间的潜在空间关系。多任务学习网络可以同时执行多个任务，并通过使用深度神经网络来挖掘训练数据的潜在特征。本研究基于顺北地区地质背景构建断岩溶体模型，并利用三维点扩散函数卷积合成断岩溶体训练数据。然后，我们开发了一个多任务学习网络来学习断层岩溶特征并同时检测断层和洞穴。测试结果表明，利用合成断岩溶数据训练的多任务学习网络能够有效识别现场地震数据中的断层和溶洞。多任务学习网络、单任务学习网络与常规方法的比较证明了断层和洞穴之间空间关系的重要性，显示了多任务学习网络的优越性。该技术对超深层碳酸盐岩油藏的勘探、开发和布井具有重要的辅助作用。