Abstract

Wear factor is an important parameter for estimating casing wear, yet the industry lacks a sufficient data-driven wear-factor prediction model based on past data—this paper discusses a method for bridging the gap. Inversion technique is a method for evaluating model parameters for a setting wherein the input and output values for the physical model/equation are known. For this case, the physical equation to calculate wear volume has wear factor, side force, RPM, tool-joint diameter, and time for a particular operation (i.e., rotating on bottom, rotating off bottom, sliding, back reaming, etc.) as inputs. Except for wear factor, these values are either available or can be calculated using another physical model (wear-volume output is available from the drilling log). Wear factor is considered the model parameter and is estimated using the inversion technique method. The preceding analysis was performed using soft-string and stiff-string models for side-force calculations and by considering linear and nonlinear wear-factor models. An iterative approach was necessary for the nonlinear wear-factor model because of its complexity. Log data provide the remaining thickness of the casing, which was converted into wear volume using standard geometric calculations.

This paper discusses an innovative data-driven approach to obtain wear factors when the wear log is available, which can be used in future drilling operations for the same well or a different yet similar well for improved casing-wear predictions. It can also be used for real time casing-wear predictions. Additionally, this method demonstrates that a single wear factor is insufficient to capture casing wear for each section of the casing. A wear-factor distribution can be provided based on available wear log data and the inversion technique.

Introduction

Casing wear, which impacts the integrity and longevity of a well, is an extremely important but less understood phenomenon in the field of drilling. Well integrity is not just limited to inside the wellbore; it also affects the outside and beyond. Hence, safety factors are added while designing casings to help prevent disasters, and an accurate prediction of casing wear can make casing designs more cost effective. One essential parameter when estimating casing wear is the casing-wear factor introduced by [1] from Maurer Engineering during 1994 as part of the joint-industry project DEA-42. The wear factors formed a vital part of the casing wear model proposed by this study, which was based on the phenomenon that when a rotating tool joint impinges against the inner wall of the casing, it forms a crescent-shaped groove on the wall by wearing out the casing. The basic assumption of this model is "the volume of steel removed from each unit length of casing at a point on the inside surface of the casing is proportional to frictional work performed at that point by the tool joint rotating in contact with the casing." It can be written in equation form as

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