Real-Time Condition and Performance Monitoring of a Subsea Blowout Preventer Pipe Ram
- Mete Mutlu (University of Houston) | Taoufik Wassar (University of Houston) | Matthew A. Franchek (University of Houston) | Ala Eddine Omrani (University of Houston) | Jose A. Gutierrez (Transocean)
- Document ID
- Society of Petroleum Engineers
- SPE Drilling & Completion
- Publication Date
- March 2018
- Document Type
- Journal Paper
- 50 - 62
- 2018.Society of Petroleum Engineers
- Model Based, Condition and Performance Monitoring, Diagnostics, Compliance Analysis, Blowout Preventer
- 6 in the last 30 days
- 259 since 2007
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Presented here is a case study on the condition and performance monitoring (CPM) of a subsea blowout preventer (BOP) pipe ram. The proposed real-time CPM solution uses adaptive physics-based models that process sensor measurements at the point of origin (known as edge analytics). The adapted model coefficients are treated as a vector, the magnitude of which estimates the degree of health degradation and the phase of which identifies its source. The benefits of using an adaptive model-based approach over traditional machine-based learning and artificial-neural-networks solutions include zero algorithm-training times, broad applicability to BOPs, model modularity, and accurate health-degradation estimates. The proposed CPM methodology is validated on a BOP pipe ram using both operational and simulated data. A sensitivity study of the method to system uncertainty is also presented.
|File Size||1 MB||Number of Pages||13|
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