The fatigue under bending and internal press is the main reason for coiled tubing (CT) failure. Because the conventional life prediction methods for estimating coiled tubing working life under multi-axial loading are not ideal, a new method was proposed to predict the coiled tubing working life by using its nonlinear approximation and generalization abilities based on the back propagation (BP) artificial neural network (ANN). The CT diameter, wall thickness and internal pressure were used as ANN training input vectors, while the experimental cycle times of the CT was used as training target vectors. A three-layer BP neutral network was built and trained by training samples. And the ANN model was verified by training samples and testing samples. The results showed that predicted values was comparatively consistent well with experimental data, moreover, the precision of this method was superior to that applied by the conventional life prediction model. The feasibility of predicting the coiled tubing working life by applying the BP ANN was proved. The above method provides a new route for developing the coiled tubing working life tracking and monitoring software and improving the application level of the coiled tubing for the field use.

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