Abstract
Fatigue is one of the most frequently encountered failure modes of rotary shouldered connections (RSC) used in drill strings. Once initiated, a fatigue crack tends to grow and ultimately lead to a twist-off, which is catastrophic and often results in lengthy non-producing time and expensive fishing operations. The complexity of the fatigue mechanism, the variabilities of material properties, and the nonlinear contact interactions of the pin and the box elements of an RSC pose a substantial challenge to accurately predicting the fatigue life of the RSC. This would require considerable conservatism to be exercised to prevent a twist-off, which causes premature retirement of drilling assets. Using a statistical approach to predict the risk of twist-off (ROTO) of each RSC on the drill string could be a more economically viable solution as it would enable quantified risk assessment and scientifically calculated tradeoffs between performance, cost, and risk of failures.
In this study, a methodology for statistical prediction of the ROTO of rotary shouldered threaded connections was developed. First, static material properties, including yield strength, tensile strength, elongation, and reduction in area, were extracted from a wealth of available material certificates. Feature engineering was carried out to arrive at two independent properties, tensile strength and reduction in area. Fatigue properties were then generated with the retrieved static material data and earlier established correlations between static and fatigue properties. Afterwards, elasto-plastic finite element analyses were performed on RSCs made of the same material but with different properties to determine critical fatigue indicators, stress and strain states as respective functions of the tensile strength. Finally, Monte-Carlo simulations were conducted with respect to statistical distributions of the two independent material variables to predict the ROTO as a function of fatigue life. The predictions were found to be favorable agreement with the available full-scale fatigue test data of an API connection type.
