# Multi-Objective Optimization Study of Annular Fluid Flow Structure in Cordless Core Drilling Tools
## 2 Structural Design and Principles of Hydraulic Lifting Cordless Coring Drilling Tool
### Principles of the Improved NSGA-II Algorithm
The combination of drilling tools was \(\varphi\)96 diamond drill bit + \(\varphi\)96 cordless core drilling tool + adapter joint + \(\varphi\)89 rope drill pipe + active drill pipe. The test drilling pressure was 14-18 KN, the rotating speed was 260 rpm, the pumping volume was 145 L/min, and the pumping

Figure 9: A physical drawing of \(\varphi\)96 hydraulic lift cordless core drilling tool. pressure was 0.3-0.5 Mpa. A complete coring was achieved. The entire test system, as shown in Figure 9(a), enabled the switching between normal and reverse circulation through ball valves 1# and 2#. As shown in Figure 9(c), the coring test was conducted for the \(\varphi\)96 drilling tool. The hydraulic salvage test of the \(\varphi\)96 drilling tool was carried out two times, mainly to verify the feasibility of hydraulic lifting and salvage under the condition of a small annular space gap in the stratum of slight leakage (small leakage or ordinary seepage). Due to the limited test conditions, only the hydraulic salvage test was carried out for the vertical holes. The visualization bench test was carried out for the large inclined holes or the horizontal holes at a later time. The hydraulic salvage test and the core drilling test used the same mud pump with a pump volume of 145 L/min and a pump pressure of 0.1-0.2 MPa when pushing the salvage in reverse circulation. Due to the shallow depth of the well, which was only less than 20 m, the assembly in the drilling tool was pushed to the hole in 40-50 s. The hydraulic lifting test of the \(\varphi\)96 drilling tool on site is shown in Figure 9(c). ## 6 Conclusions and Discussion

In this research, a new type of hydraulic lifting cordless core drilling tool was designed, and a numerical simulation model of the liquid flow in the annulus of the spearhead and spool of the cordless core drilling tool was established. Moreover, an orthogonal test was carried out, and according to the results of the orthogonal test, a numerical prediction model of the annulus of the spool based on the BP neural network was established. The NSGA-II algorithm was optimized, and multi-objective optimization was carried out for the annulus of the cordless core drilling tool. According to the parameters of the optimized structure, the \(\varphi\)96 cordless core drilling tool was developed, and finally, the simulation and field test were carried out. Optimized structural parameters and the \(\varphi\)96 cordless core drilling tool were developed. Finally, simulation and field tests were carried out. The following conclusions could be drawn:

(1) According to the established numerical simulation model of spearhead and spool, different parameters such as the number of spearhead inlets, the diameter of spearhead inlets, the number of spool outlets, the diameter of spool outlets, and the center distance between the two circles of spool outlets could affect the ratio of fluid back pressure to

Figure 9: Field tests for coring and hydraulic lifting using the cordless coring drilling tool. spring force pressure and the flow rate of the drilling fluid. The numerical prediction model of the spool's annular fluid field with a BP neural network was established, which demonstrates the effect on the flow rate of the drilling fluid when the parameters of the structure of the spearhead and spool were different. The numerical prediction model of the BP neural network of the spool annulus flow field was established to predict the drilling fluid flow rate and spool back pressure ratio at different parameters of the spearhead and spool annulus flow structure. The average correlation coefficient of the prediction was R = 0.93, which provided a basis for the design of hydraulic lift cordless coring drilling tools for drilling in different formations. (2) The NSGA-II algorithm was optimized. The Pareto front of the improved NSGA-II algorithm has a more uniform and wider distribution in general.