Axial and bending very high cycle fatigue of completion string

Abstract

Preventing or mitigating very high cycle fatigue (VHCF) damage and failure of completion string is essential for ensuring their long-term stable operation. Therefore, a comprehensive understanding of the string's fatigue performance in tension, compression, and bending over an extended service life is necessary. This study investigated the evolution, microscopic mechanisms, fracture behavior, and life prediction of VHCF damage in completion string under axial and bending conditions. It was found that the intragranular twin structures on the critical interface of VHCF source directly participate in lattice reconstruction, enhancing the grain's strain coordination ability, and increasing the proportion of facet area on the fatigue fracture surface. Based on the fracture characteristics of VHCF, the Mayer empirical formula was improved, and a novel convolutional neural network (CNN) based VHCF life prediction model was proposed. Utilizing the CNN's powerful fracture feature extraction and nonlinear modeling capabilities, the VHCF fatigue life prediction accuracy was significantly enhanced, with prediction errors all within 2 times the experimental results' error range. Based on the axial and bending VHCF fracture mechanisms of completion string and CNN, a more advanced method for predicting axial and bending VHCF life is provided, offering theoretical supports for the engineering safety service and life assessment of completion string.