Fault recognition method and application based on 3D U-Net++convolution neural network
Fault interpretation is the basis and key to seismic data interpretation,and accurate and reasonable fault identifi-cation plays a vital role in oil and gas exploitation.With the increasing demand of oil fields for fault interpretation accuracy,the accuracy of traditional fault interpretation methods based solely on artificial attributes such as coherence,curvature,etc.,can-not meet the requirements.Based on the U-Net convolution neural network model,this paper proposes an automatic fault recognition method,which can automatically extract faults from any 3D seismic image.In this paper,the model carries out au-tomatic fault identification on the actual seismic data of two blocks under the training of sufficient sample sets and analyzes and compares the identification results.The experimental results show that the model can automatically recognize faults from arbitrary 3D seismic data,and the fault recognition results based on the 3D U-Net++network model have significantly improved the accuracy of the recognition results compared with the traditional U-Net network.It also shows a good effect on the recognition of minor faults in-side the buried hill and significantly improves the efficiency of conventional and complex fault recognition.
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