An Intelligent Recognition Method for Low-Grade Fault Based on Attention Mechanism and Encoder–Decoder Network Structure
Author:
Zhang Yujie,
Wang Dongdong,
Ding Renwei,
Yang Jing,
Zhao LihongORCID,
Zhao ShuoORCID,
Cai Minghao,
Han Tianjiao
Abstract
Low-grade faults play an important role in controlling oil and gas accumulations, but their fault throw is small and difficult to identify. Traditional low-grade fault recognition methods are time-consuming and inaccurate. Therefore, this study proposes a combination of a simulated low-grade fault sample set and a self-constructed convolutional neural network to recognize low-grade faults. We used Wu’s method to generate 500 pairs of low-grade fault samples to provide the data for deep learning. By combining the attention mechanism with UNet, an SE-UNet with efficient allocation of limited attention resources was constructed, which can select the features that are more critical to the current task objective from ample feature information, thus improving the expression ability of the network. The network model is applied to real data, and the results show that the SE-UNet model has better generalization ability and can better recognize low-grade and more continuous faults. Compared with the original UNet model, the SE-UNet model is more accurate and has more advantages in recognizing low-grade faults.
Funder
Natural Science Foundation of Shandong Province
Major Research Plan on West-Pacific Earth System Multispheric Interactions
National Natural Science Foundation of China
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
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