Reconstruction method of logging curves by 2D convolutional neural network integrating attention mechanism
ZHAI Xiaoyan1,2, GAO Gang1,2, LI Yonggen3, CHEN Dong4, GUI Zhixian1,2, WANG Zhizhen1,2
1. Key Laboratory of Exploration Technologies for Oil and Gas Resources(Yangtze University), Ministry of Education, Wuhan, Hubei 430100, China; 2. College of Geophysics and Petroleum Resources, Yangtze University, Wuhan, Hubei 430100, China; 3. Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China; 4. Petroleum Exploration and Production Research Institute, Sinopec, Beijing 100083, China
Abstract：The density and acoustic time-difference logging curves are two important curves that bridge seismic and rock physics, and they are the only logging curves that can provide reliable full-band elastic information for well-constrained seismic inversion. However, in practical applications, factors such as borehole collapse and instrument failures often result in distortion or missing data in density and acoustic time-difference logging. Moreover, existing empirical model methods, multivariate fitting methods, and rock physics modeling methods have limitations in accurately reconstructing target curves, especially when dealing with the simultaneous reconstruction of both curves. Therefore, this paper proposes to integrate the attention mechanism into the two-dimensional (2D) convolutional neural network to enhance the ability of the deep neural network (DNN) to capture the autocorrelation and cross-correlation features of the logging curves, so as to improve the accuracy of the DNN to reconstruct the acoustic and density logging curves. The ultra-deep tight sandstone in Junggar Basin is taken as the research object. Firstly, the relationship between the autocorrelation and cross-correlation features of logging curves and the weight distribution of the attention layer is analyzed. A comparative analysis is then conducted to assess the prediction accuracy of the proposed network against gated recurrent units (GRU) and 2D convolutional neural networks. The structural parameters of the proposed network are also optimized. Finally, the correction and reconstruction of the target curves are validated using synthetic seismic records, demonstrating the high prediction accuracy of the proposed network.
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