Abstract
With the accelerating development of the Chinese economy, the demand for oil and gas energy is increasing day by day. The accuracy and efficiency of fault interpretation are crucial for the exploration and development of oil and gas reservoirs. The presence of oil and gas in reservoirs will inevitably cause changes in the geophysical response characteristics, and different types of reservoirs have different response characteristics in logging curves such as sound, discharge, and electricity. 3D seismic technology is currently one of the most effective techniques for obtaining fault structural features and identifying small faults, but conventional seismic data processing techniques are unable to meet the accuracy requirements of current seismic exploration. With the development of computer technology, more and more scholars are applying deep learning (DL) algorithms to the identification of faults and reservoirs to further improve the recognition effect. This article is based on the DL algorithm and utilizes 3D seismic technology to design a method for automatic and accurate recognition of fault and reservoir features. Obtain labeled seismic amplitude faults, reservoir data, and seismic amplitude data to be identified from the 3D seismic amplitude data volume, and construct a CNN training model to identify the fault and reservoir data. The experimental results show that the method designed in this article can improve the efficiency and accuracy of fault and reservoir identification.