This paper addresses the probabilistic analysis of casing tubulars, regarding the failure modes defined in API 5C3 code, which refers to the violation of elastic regime due to internal and external pressures, and axial force. The casing system performs important structural and isolation functions, ensuring the well integrity through its life cycle. The reliability-based casing design handles rigorously the uncertainties associated with the tube manufacturing, as variations in geometrical and mechanical properties, allowing to evaluate the probability of failure. It is presented a parametric analysis over different steel grades and tube slenderness, besides the application to a design scenario, by using Monte Carlo simulation and firstorder reliability method. The results indicate that: collapse is the dominant failure mode; wall thickness and the yield limit govern the probabilistic response; the triaxial envelopes, revisited in a probabilistic framework, consist in a powerful tool, supporting the decision-making process in casing design.

No abstract

No abstract

No abstract

Summary The inspection of casing integrity is an important field of study regarding well safety assessment. In casing design, casing wear prediction is usually performed with a torque and drag model calibrated with laboratory simulation results. The availability of ultrasonic logging data makes it possible to calibrate these models, correlating actual conditions with similar equipment, operations, and trajectory. This work presents an accurate methodology that quantifies casing wear under a certain uncertainty level using data from ultrasonic logging tools. An ellipse equation is adopted to estimate the prewear condition of the cross section, and then it is applied to calculate wear. Also, a methodology to generate synthetic tube cross sections with prescribed ovality, eccentricity, and groove wear, with any intensities and locations, is presented. A model error analysis is carried out to compare the accuracy of the proposed strategy with others found in the literature. A statistical error analysis shows how the measurement noise is related to the estimated wear. This provides the level of uncertainty of the response, which can improve the right interpretation of data. Results demonstrate that the strategy successfully achieves an adequate quantification and can be applied to estimate casing wear profiles, under an admissible uncertainty tolerance.