Improving Casing Wear Prediction and Mitigation Using a Statistically Based Model

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. Project GoalThe goal of this project was to more accurately quantify casing wear risk by improving casing wear analysis accuracy. To do this, data from a large number of wells was analyzed to generate probabilities for dog-leg severity in common well types and also correlate those to actual backmodeled casing wear factors. The results will allow an engineer to analyze what the expected casing wear might be for an average (P50) horizontal well, and then evaluate the maximum expected wear for a 1 in 10 case (P90), 1 in 20 case (P95), or 1 in 100 (P99) case. SurveysAll casing wear software, and torque and drag software as well, use a directional survey to determine the side force or contact force between the drill string and wellbore. These points within a directional survey can be a representation of a planned well path, or it can be taken from actual downhole measurements. The survey points are then connected into a single line representing a best approximation of the wellpath with the information given.

Casing Wear and Wear Factors: New Experimental Study and Analysis

“…In general, for water-based drilling fluids with a steel-tool joint, wear factor values are between 0.5 and 40 (10 −10 psi −1 ) or 7 to 580 (10 −9 MPa −1 ). However, for oil-based drilling fluids with a steel tool-joint the wear factor values are between 0.3 and 5 (10 −10 psi −1 ) or 4 to 73 (10 −9 MPa −1 ) [ 25 , 26 ]. The present wear factor values were within the lower limits of the above reported normal ranges.…”

A new method for predicting casing wear in highly deviated wells using mud logging data

“…[9] Mitchell and Xiang examined the effect of tortuosity and the use of a rotary steerable system in decreasing dogleg to diminish wear volume. [10] Gao and Sun developed a model based on contact pressure and showed that less wear occurs with increasing tool joint external diameter. [11] Sun et al reduced the wear rate by adding limestone to the drilling fluid.…”

Experimental and numerical study on casing wear in highly deviated drilling for oil and gas

“…Gao and Sun 14 modified the wear efficiency model by considering the change in the contact pressure during casing wear process and developed the nonlinear relationship of the wear depth and time. Through a wide variety of wells with measured depths (MDs) greater than 13,000 ft, Mitchell and Xiang 15 created an extensive database and presented an improved casing wear prediction model using the statistically based model. Through the comparisons of different experimental tests, Sun et al 3 established a nonlinear wear model to consider running-in casing wear and steady-state casing wear during pipe rotation.…”