Prediction of Casing Collapse Resistance by 3D Finite Element Modeling

Abstract

Summary 3D outside diameter (OD) and wall thickness mapping of a casing joint consists of synchronized OD laser scanning and ultrasonic testing (UT) wall thickness measurement at a resolution of every 1 in. along the length and every 1° or 2° around the circumference. This full-length mapping data can then be used to construct a 3D finite element analysis (FEA) model that captures the geometric attributes, including OD, wall thickness, OD ovality, and wall eccentricity, of a casing joint. Together with the actual nonlinear material plasticity model and the measured as-manufactured residual stress, the 3D FEA model can predict the casing collapse strength with good accuracy. Also, the Klever-Tamano ultimate limit-state (KT ULS) collapse equation per API TR 5C3 (2018) together with a moving-average (MA) scheme can calculate the joint’s collapse strength. Results predicted by 3D FEA and KT ULS collapse models are compared to the actual full-scale collapse test results. A 3D FEA model generally produces more accurate results than the KT collapse equation. In addition, both 3D FEA and KT collapse models predict a collapse pressure slightly lower than the actual collapse pressure, which is therefore on the conservative side. The prediction of casing collapse strength can be beneficial to the end user in sorting and ranking the casing joints by collapse resistance, and therefore provides valuable information that can be used in the end user’s risk assessment. Furthermore, the end user may potentially select the joints with the highest predicted collapse strength for the portion of the casing string where the highest collapse resistance is desired. In addition, the OD and wall mapping data together with the actual material tensile properties (yield strength, ultimate tensile strength, and strain hardening parameters) can be used in estimating the internal yield, ductile rupture pressures, and other performance properties. This is outside the scope of this paper and can be pursued in future work. These predicted casing performance properties may then be used in sorting and ranking the joints based on a specific performance property of interest or a combination of performance properties.