Risk based statistical approach to predict casing leaks

This paper will present a statistical risk based approach to proactively predict casing leaks using Electromagnetic (EM) corrosion logs. The corrosion growth of downhole casing hotspots (areas likely to develop casing damage) is monitored to develop expectancy calculations of a typical well's remaining life. Currently the predominant technology used for casing integrity measurement and monitoring are the EM corrosion logging tools. While this technology has provided a step-change in the ability to measure and monitor corrosion, the findings are not usually conclusive and need to be integrated with other data to make qualitative assessment. This is largely due to the nature of the tool's output where averaging is used to assess metal loss rather than direct measurement of the spot where metal loss is taking place. In other words, 50% average metal loss could mean a failure if one part of the casing is completely gone and the other is intact; or 50% metal loss is distributed evenly across the 360 degree circumference of a casing with no leak. This wide range of possibilities and uncertainty has made it extremely challenging to both interpret and analyze EM corrosion logging data. Establishing consistent criteria to classify the corrosion severity and confidently decide on the need to workover the well or not is a challenge to all field operators worldwide. A probabilistic approach was introduced to improve EM corrosion logs' data interpretation. More than five hundred data points were collected and statistically analyzed to build a probability of failure model as a function of EM average metal thickness loss. These models were used to delineate a dynamic safe window of the average metal loss value across multiple casings.