Coiled tubing (CT) erosion can occur during CT fracturing operations. Resulting wall loss and fatigue can limit CT life and prevent safe wellsite operations. An artificial neural network (ANN) has been successfully developed for accurately predicting wall loss resulting from erosion.

This paper presents a case history in which ANN technology was used to successfully manage tubing strings during CT fracturing operations. During these operations, wall loss affects CT pressure ratings, tensile strength, and fatigue, all of which are critical performance parameters used for determining CT life and identifying a safe operating envelope. An ANN can predict erosional wall loss and quantify critical performance parameters for specific applications.

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