Towards Automated Condition Monitoring of Blowout Preventer Wellbore Packers

Abstract

According to Bureau of Safety and Environmental Enforcement (BSEE), in 2017 a total of 45 out of 59 rigs operating in the Gulf of Mexico reported component failures of well control related equipment. The aftermath of the oil spill from the Deepwater Horizon rig provides stark illustration that acceptance of the equipment failure status quo is untenable. In this work, the authors propose novel automated strategies to monitor the health of blowout preventers (BOPs) on offshore drilling rigs. Using physicsbased models, we demonstrate computational detection of pressure tests by identifying characteristic features in timeseries pressure data. After detection, we present a methodology for extracting features relevant for prognostic health monitoring, including pressure decay and hold durations. Augmenting these computational models with domain knowledge from BOP experts, we produce health indices (HIs) for the respective equipment as the output. In addition, we demonstrate the optimized enumeration and identification of possible pressure test plans for BOPs in different combinatorial configurations. By combining our novel detection approach with health indices and automated test planning, this work contributes to prediction and amelioration of well control equipment failures on offshore drilling rigs.