Identifying casing failures with signature fracturing treatment pressurebehavior

Abstract

One of the challenges of unconventional resource development is the identifying and preventing casingfailures caused by the hydraulic fracturing process. Multiple mechanisms may be responsible for casingdeformation and/or failures, starting with the rock properties of the formation, the wellbore configuration,quality control of tubulars, and operational aspects during drilling and completion. This paper presents twocase studies where casing issues were discovered during the drill out of frac plugs following multi-stagefracturing treatments. The objectives of these studies are (a) to determine the cause and nature of the casingfailures, (b) to recommend changes to future completion programs to prevent similar operational issues, and(c) to develop a model that automatically identifies these failures. The subject wells are located in two very different basins: The Eagle Ford trend in the Brazos Valley (BV)area of south Texas and the Powder River Basin (PRB) in Wyoming. In both studies, the casing issues couldbe directly correlated to Abnormal Pressure Behaviors (APBs) observed during fracturing. A total of 486stages, completed in 12 different wells, were reviewed using a cloud-based application that allows stagesto be examined individually, or as groups. Since then, five additional wells have been added to the data set.After problem stages were identified, the completion team worked with the drilling engineers and geologiststo determine the mechanisms causing the casing damage. Tight spots encountered during frac plug drill out in the BV wells directly correlated with stagescompleted in geological transition zones between the Eagle Ford and Woodbine formations. Once thiswas recognized, the team implemented operational contingencies to fracture designs for stages completedin BV transition zones. In the PRB wells, after reevaluating the post-mill inspection of the casing, thedamage was found to be poor casing quality control. The location of casing deformations and/or failuresdirectly correlated with stages that displayed evidence of frac plug failure. Moving forward, the PRBcompletion supervisors were made aware of potential issues, and alternative procedures were developed forboth fracturing and drill out operations that utilized the questionable casing. As of this time, no additionalcasing issues have occurred. In these studies, identification of the problem stages was initially performed manually (stage-by-stage)using a cloud-based analytics platform (CBAP). During the process, it was recognized that the two types ofproblem stages had their own characteristic pressure signature. A machine learning algorithm was developedthat automatically identifies plug failure, which is indicated by a sudden unexplained pressure drop in theabsence of rate changes. Transition stages could be easily identified through the use of stage variance plots(e.g., comparing maximum/average rates and pressures across multiple stages and wells) and also throughmachine learning algorithms that identified unexpected pressure increases followed by sharp pressure drops.