## 4 Conclusions  
In this paper, candidate models of sedimentary facies with different channel widths were optimized by using a deep learning method, and the channel widths in the study area were finally determined using comparative analysis. Three points of understanding are as follows:  
1. A deep convolutional neural network implemented the channel width optimization method, taking the candidate model as the population and randomly sampling it many times, ensuring that the sampling density is equal to the conditional data density. If the difference between the spatial characteristics of the sample points and the conditional data is smaller, the spatial characteristics of the model and the conditional data are considered to be more similar. After testing, the candidate models with different widths were identified as the corresponding channel width models, with over 95% accuracy in the 2D model. Compared to the two-dimensional model, the accuracy of the model training of the method in three-dimensional space was lower, but still above 80%. This shows that the method is sensitive to river channel width.
2. A comparison of the method with the MDevD-based method was carried out to verify the practicality and reliability of the method, and it was demonstrated that the recognition rate of this paper's method closely matches the MDevD method's recognition rate, indicating the reliability of the proposed method. In the process of conducting 100 tests, using the MDevD method took a lot of time, with the total time consumption reaching 13.7 h, while the method proposed in this paper took 53 s, indicating that the algorithm in this paper marks a great improvement in computational efficiency, and has a high degree of practicality. It can be used to analyze the channel width of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Channel Model** & **Min. Width (m)** & **Mean Width (m)** & **Max. Width (m)** \\ \hline Model a & 90 & 100 & 110 \\ Model b & 120 & 130 & 140 \\ Model c & 150 & 160 & 170 \\ Model d & 180 & 190 & 200 \\ Model e & 210 & 220 & 230 \\ Model f & 240 & 250 & 260 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Triangular distribution of width values for six channel sand body candidate models.  
the actual work area, and provide more accurate guidance for oilfield development to formulate a more reasonable development plan.
3. Based on the geological understanding and previous experience, the approximate range of channel width in the study area was determined to be between 100 and 250 m, and accordingly, candidate models with channel widths of 100 m, 130 m, 160 m, 190 m, 220 m, and 250 m were designed. Using the deep convolutional network-based channel width optimization method, candidate models for different channel widths in the research area were compared and analyzed, and the results showed that when the channel width was 160 m, it was most compatible with the conditional data in the research area. The quantitative analysis of the width of the underwater diversion channel provides a basis for the study of the fine inhomogeneity of the reservoir, which is of practical significance for the inverse deduction of the width of the river sands and the distribution characteristics of the actual workings based on the well-point data, and also provides a basis for multi-point geostatistical stochastic modeling.  
Methodology, J.W.; Writing--original draft, J.W.; Writing--review & editing, S.L.; Visualization, S.L.; Supervision, S.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Open Fund of the Key Laboratory of Exploration Technologies for Oil and Gas Resources (Yangtze University), Ministry of Education (No. PI2023-04) and the National Natural Science Foundation of China (No. 42002147).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.