Real-Time Detection of Under-Reamer Failure: An Example of Agile Data Analytics Development and Deployment
- Yu Liu (Shell) | Yanbin Bai (Shell) | Sean Wu (Shell) | Manny Martinez (Shell)
- Document ID
- The Society of Naval Architects and Marine Engineers
- SNAME 23rd Offshore Symposium, 14 February, Houston, Texas
- Publication Date
- Document Type
- Conference Paper
- 2018. The Society of Naval Architects and Marine Engineers
- Underreamer, Machine Learning, Deepwater Drilling, Data analytics
- 2 in the last 30 days
- 21 since 2007
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In response to the lower for longer oil price environment and the rapidly changing digital opportunities, agile development and deployment of digital solutions using data analytics can become essential in collecting short term business value through cost reduction and building flexible technology infrastructure for long term digital transformation. The new agile development/deployment adopted by the company consists of the following process: identify operational need, develop digital solutions through a combination of conventional physics based model and machine learning techniques, deliver through a flexible real-time web platform, and feedback learnings from deployment to facilitate next round of development. ReamerVision, the real-time early detection of under-reamer failure during hole enlargement while drilling at deepwater Gulf of Mexico is one of the successful examples:
- Operational need identified: Two active cutting structures (drill bit and expandable under-reamer) are often used for hole enlargement while drilling in Deepwater. A potential operational failure is the under-reamer may subject to premature cutter damage while drill bit is still in good condition. Without detection of this failure, an undergauge hole might be drilled and causes difficulty for subsequent casing running.
- Data analytics methodology: Historical data of both surface and downhole mechanics data are collected. Several metrics based on physics model are constructed to quantify cutter conditions, drilling efficiency, and formation variation. Supervised learning of historical dataset processed by those metrics yields thresholds for failure detection in real-time.
- Deliverable format: a web-based real-time data platform is constructed for deployment of this technology. A workflow is established between remote control center, drilling engineers, and domain-experts to establish real-time monitoring of under-reamer condition and provide actionable information to operation.
- Continue improvement: since April 2016, ReamerVision has been deployed to 27 deepwater reamer runs as a risk-mitigation tool. Positive feedbacks from end-users are utilized for calibration of model and development of new features.
|File Size||527 KB||Number of Pages||6|