Fatigue life assessment for incremental innovation of novel O&G equipment using a calibrated finite element and Monte Carlo samplings

Fatigue failures are one of the most common and critical causes of operational loss of functionality in systems and equipment in the oil and gas (O&G) industry. The fatigue damage analysis has been classically used based on the S – N curve, the Palmgren–Miner, and cycle-counting procedures (e.g., rainflow counting). Alternatively, methods in the frequency domain may also be applied to evaluate the damage dealing with the signal power spectral density. However, for design changes in systems already develop (i.e., incremental innovation), traditional physical tests in component and sub-systems may not be feasible. Hence, finite element models (FEM) are employed to provide failure analysis and engineering information using numerical methods. Nevertheless, FEM provides deterministic results, and a stochastic mechanism is introduced to account for the variability of real environments. In addition, there is an epistemological uncertainty related to the parameters in the stochastic FEM, which have to be modeled and processed. In this paper, we consider a numerical experiment to estimate the fatigue life of an open-hole expansive packer with feedthrough lines. In this case, the random loading is obtained through a numerical finite element analysis calibrated with experimental data, to which frequency-domain and stochastic damage evaluation based on a Gaussian distribution is applied. Monte Carlo samplings are carried out to obtain lifetime probability distributions. Moreover, the model’s uncertainty is taken into account by varying the stochastic parameters of the experiment in the developed model. Based on the proposed methodology, the fatigue life for the control lines is greater than 1000 years for this O&G equipment. Indeed, the achieved neglectable risk of fatigue failure is in agreement with the opinion of several O&G experts consulted.