# A Model-Based Intelligent Adjustment Method of Toolface for Bent-Housing Motor
## 1 Introduction
[PERSON] [9] established the dynamic equation of the drill pipe torsion process, carried out indoor experiments according to the model similarity theory, and obtained the parameter similarity conditions and scale corresponding relationship between the experimental model and the drilling prototype. On this basis, the expert PID method was used to realize the automatic control of the toolface. The principles of the drill string dynamics models related to toolface adjustment are clear and easy to understand. However, the models have made some idealized assumptions and simplifications, and have not yet been validated by on-site data. The actual situations of the drilling site are not completely consistent with the drill string dynamics models, which makes the calculated value of the models deviate from the actual value. For an 8000 m deep directional well, when the wellhead drill string rotates 360\({}^{\circ}\), the actual value of the downhole toolface differs by up to 50\({}^{\circ}\) from the calculated value of the drill string dynamics model; when the wellhead drill string rotates 720\({}^{\circ}\), this difference can reach 150\({}^{\circ}\)[10]. The artificial intelligence method can fit the relationship between the physical model results and the actual data in the drilling site well. A BP (back propagation) neural network, based on a GA (genetic algorithm), has a good fitting effect on nonlinear functions affected by multiple parameters, has been widely used in the drilling field, and has achieved satisfactory results in recent years, such as build-up rate prediction [11], crude oil output decline rate prediction [12], crude oil production prediction [13], overflow and leakage prediction [14; 15; 16], bottom hole pressure prediction [17], horizontal in situ stress and natural fracture property identification [18], oil saturation prediction [19], drilling tool structure optimization [20], optimal ROP calculation, and drilling parameter optimization [21]. The downhole toolface change value during the adjustment of the bent-housing motor toolface can be regarded as a nonlinear function affected by multiple parameters, and its influencing factors at least include well trajectory, wellbore structure, drilling assembly, drilling fluid property, bit performance, formation characteristics, the interaction between bit and formation, bent-housing motor performance, and drilling engineering parameters. In this paper, the toolface change value during the toolface adjustment process of the bent-housing motor is the research object, and intelligent prediction models are established to predict the change value of the toolface, so as to provide adjustment parameters for field operators. Based on the drill string dynamics model, the downhole toolface change value of the bent-housing motor is calculated when the rotary parameters of the wellhead drill string are known. Then, the actual measured change value of the toolface is taken as the output parameter, the drill string dynamics model calculates the change value of the toolface, and the factors that have a significant impact on the change value of the toolface are taken as the input parameters; the BP neural network based on GA optimization is used to fit the relationship between the input parameters and the output parameter. The fitted function will be used to predict the actual change value of the toolface in the later drilling operation. At the same time, the GA-BP method is used to fit the relationship between the reactive torque rotation angle and its influence factors. The prediction results of these two intelligent fitting functions will be used to formulate the adjustment parameters of the toolface in advance. This intelligent method will contribute to the development of bent-housing motor toolface adjustment from experience to refinement, promote the application of artificial intelligence in the drilling field, and help to improve the drilling speed of directional wells and horizontal wells.