Xiaofeng Wang

Expansion screen pipe is a new sand control completion technology. In this paper, the optimization design of the slotted structure of the screen pipe is studied. Mechanical models for both seamless base pipes and slotted base pipes have been established based on elastic-plastic mechanics. The finite element simulation analysis of the expansion process of screen pipes with different slotted structures is carried out, and the relationship among the length of the slot L, the number of slots N, the internal pressure P and the maximum stress at the slot end σ is obtained. The principle and optimization process of the NSGA-II algorithm are introduced. Furthermore, a multi-objective optimization model is formulated, taking P and σ as the objective functions, and the NSGA-II algorithm is applied to optimize this model. By analyzing the obtained optimization results for the screen pipe with an outer diameter of 68 mm, the optimal slotting parameters are L = 137 mm and n = 12. For the optimal slotted structure obtained, P is greater than the minimum expansion initiation pressure, and σ is less than the ultimate tensile strength, which indicates that the optimal solution meets the requirements of both theoretical analysis and engineering practice. In this paper, the NSGA-II multi-objective optimization algorithm is introduced into screen pipe structure optimization for the first time. The structural optimization method of expandable screen pipe proposed in this paper improves the design level and efficiency of the screen pipe. At the same time, it provides a new means for research on screen pipe structures in the fields of petroleum and exploration.

The connection between the steel joint and aluminum alloy pipe is the weak part of the aluminum alloy drill pipe. Practically, the interference connection between the aluminum alloy rod and the steel joint is usually realized by thermal assembly. In this paper, the relationship between the cooling water flow rate, initial heating temperature and the thermal deformation of the steel joint in interference thermal assembly was studied and predicted. Firstly, the temperature data of each measuring point of the steel joint were obtained by a thermal assembly experiment. Based on the theory of thermoelasticity, the analytical solution of the thermal deformation of the steel joint was studied. The temperature function was fitted by the least square method, and the calculated value of radial thermal deformation of the section was finally obtained. Based on the BP neural network algorithm, the thermal deformation of steel joint section was predicted. Besides, a prediction model was established, which was about the relationship between cooling water flow rate, initial heating temperature and interference. The magnitude of interference fit of steel joint was predicted. The magnitude of the interference fit of the steel joint was predicted. A polynomial model, exponential model and Gaussian model were adopted to predict the sectional deformation so as to compare and analyze the predictive performance of a BP neural network, among which the polynomial model was used to predict the magnitude of the interference fit. Through a comparative analysis of the fitting residual (RE) and sum of squares of the error (SSE), it can be known that a BP neural network has good prediction accuracy. The predicted results showed that the error of the prediction model increases with the increase of the heating temperature in the prediction model of the steel node interference and related factors. When the cooling water velocity hit 0.038 m/s, the prediction accuracy was the highest. The prediction error increases with the increase or decrease of the velocity. Especially when the velocity increases, the trend of error increasing became more obvious. The analysis shows that this method has better prediction accuracy.