Unlocking Reserves in a BP Operated High-Pressure Gas Field through Reliability Based Casing Design

摘要

The BP project team has considered increased reserves recovery by lowering the reservoir abandonment pressure below the initial design value. Through a multi-disciplinary approach, design assumptions and equipment ratings were systematically reviewed to determine which aspects factored into the decision to change reservoir management. Collapse loading of the 10 in. production liner was identified as a key variable. The conventional design factor, a ratio of the design load to the API collapse rating, was deemed to be an insufficient way of characterizing design margin, primarily due to the perception of conservatism in the rating. While design factors are convenient for screening a casing string against an agreed-upon set of inputs and assumptions, there is little insight gained from comparing a 1.03 design factor to a 1.02 other than one value is higher than the other. The team embarked on a scope of work to characterize the probability of collapse as a function of reservoir abandonment pressure using reliability based design (RBD). Physical testing was conducted to characterize the distribution of collapse resistance and the distribution of dimensional and strength parameters which govern collapse. The quality data sets are combined using the Klever-Tamano limit state equation to indirectly derive a distribution of collapse resistance. The destructive collapse tests provide both a direct measure of the distribution of collapse and a way to calibrate the limit state equation model uncertainty. Both the direct and indirect methods are useful in determining the probability of collapse for a design load. Load uncertainty was characterized by considering variability of conditions across the wellstock, including depth, temperature and completion configuration. Casing wear was also considered in the assessment. This paper outlines the RBD methodology used to support the decision to lower reservoir abandonment pressures. Details on how to construct the statistical collapse model are provided along with a discussion on interpretation and continuous improvement activities.