Prediction of the Deformation of Aluminum Alloy Drill Pipes in Thermal Assembly Based on a BP Neural Network

Xueqi Wang,Baochang Liu,Haoliang Bai,Xiaofeng Wang,Jiaqi Yun

doi:10.3390/app12020757

Abstract

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.

Highlights

IntroductionThe steel drill pipe was widely used in traditional drillings
The results showed that compared with other heat load forecasting methods, the Back propagation (BP) neural network had obvious advantages in predicting accurately, and this model resulted in a good heat load forecasting effect
The connection between the steel joint and the aluminum alloy pipe is the weak part of the aluminum alloy drill pipe and the main site of its failure

Summary

Introduction

The steel drill pipe was widely used in traditional drillings. Owing to the high density of steel, the drilling depth was limited. With the increase of drilling depth, the weight of the steel drill pipe increased rapidly, resulting in a significant increase in the requirements for the capacity of drilling equipment. For deep continental drilling and deepwater offshore drilling, it was very necessary to use light aluminum alloy drill pipe to improve drilling efficiency and reduce power consumption. Using an aluminum alloy drill pipe, more portable drilling equipment could be used at the same well depth, which greatly saved costs. For the aluminum alloy drill pipe, the connection between the steel joint and aluminum alloy pipe was the weak part, and the main failure part of aluminum alloy drill pipes. The connection reliability would directly affect the reliability of the whole drill pipe

Methods

Results

Conclusion