Improving Casing Wear Prediction and Mitigation Using a Statistically Based Model

Abstract

Abstract Wells are now routinely drilled both in deepwater and on land to depths that were previously considered impossible. In these environments, casing design is critical to safely and successfully drilling and producing wells, and unexpected casing wear can result in significant costs or even the loss of a well. As part of a successful casing design strategy, the engineer must assess the maximum permissible casing wear required to maintain casing integrity. Then, steps must be taken to ensure that casing wear thresholds are not exceeded. Casing wear models use the number of drill string revolutions and contact force between the drill pipe and casing to calculate wear. The contact force is calculated using the dog-leg severity within the well, with the maximum dog-leg severity often determining the location and extent of the most severe casing wear. There is often a large discrepancy between predicted and actual casing wear because of survey quality and inaccurate estimates of dog-leg severity and total revolutions. These discrepancies result in predictions of contact force and drill string revolutions that are in error by 50% or more. To improve the accuracy of casing wear models, an extensive database was created from a wide variety of wells with measured depths greater than 13,000ft. The database results in a statistically based model for determining dog-leg severity within vertical, build, and tangent sections, as well as total drill string revolutions at various levels of confidence to bound average and maximum expected contact force and casing wear. Case histories compare measured wear with predictions of casing wear based on original well data and the statistically based model. The case histories also demonstrate the effect of various drilling parameters on casing wear, and evaluate the effectiveness of non-rotating protectors in preventing casing wear.