## 4 Conclusions  
This study used reverse modeling methods to obtain the structural characteristics of drill bits and established a prediction model for drill bit depth and permeability using the BP neural network. The following conclusions can be drawn:  
1. The number of blades, cutting angle of cutters, crown top rotation radius, internal cone angle, and diameter of cutters are the main structural characteristics that affect the bit's ROP and footage. The cutter-arrangement method and the design of crown rotation radius and internal cone angle are the main optimization objectives to improve the bit's performance. However, the degree of effect varies depending on the formation conditions. The cutting angle of cutters has a bigger influence on the ROP than the crown rotation radius and internal cone angle in formations with high abrasiveness and poor drill ability. Still, the inverse is true in formations with good drill ability. The drill bit structure can be reasonably optimized based on geological conditions.
2. The blade number, crown top rotation radius, inner cone angle, and cutting angle of cutter have a greater influence on the ROP than the footage, whereas the thickness of blades, gauge length, gauge width, gauge diameter length, and equivalent diameter of nozzle have a greater influence on the footage than the ROP, allowing the bit structure to be reasonably optimized based on field operation requirements. The drill bit structure can be reasonably optimized based on on-site operation requirements.
3. Based on analyzing and obtaining the characteristic parameters of the drill bit and the on-site usage data of the drill bit, a BP neural network is used to establish a prediction model for the mechanical drilling speed and footage of the drill bit in specific layers, and the optimal number of hidden layer units for each prediction model is obtained. The goodness of fit is greater than 85%, and the model's accuracy is outstanding. It can provide a theoretical basis for the optimization of structural characteristics and the prediction of application effect of shale gas bits in specific formations in Luzhou block, Sichuan province.  
Methodology, Y.C.; Validation, X.W.; Formal analysis, Y.S.; Investigation, Y.C., X.W., H.S. and P.W.; Data curation, X.Y.; Writing--original draft, P.W. and X.L.; Writing--review & editing, Y.C. and C.X.; Visualization, C.X.; Project administration, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research is supported by China National Petroleum Corporation's major on-site testing project \"Long life screw + high-efficiency PDC drill bit + soluble bridge plug matching technology test\" (No.: 2020F-47); Research project of PetroChina Southwest Oil and Gas Field Company, titled \"Multi factor Drilling Simulation and Jamming Warning for Shale Gas Horizontal Wells\" (No. 20210302-32).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Figure 6: Prediction results of BP neural network: (**a**) Shaximiao Formation to Ziliujing Formation and (**b**) Xujiahe Formation.  
**Conflicts of Interest**: Authors [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company PetroChina Southwest Oil & Gas Field Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.