IIoT Edge Analytics: Deploying Machine Learning at the Wellhead to Identify Rod Pump Failure
- Bartosz Boguslawski (Schneider Electric) | Matthieu Boujonnier (Schneider Electric) | Loryne Bissuel-Beauvais (Schneider Electric) | Fahd Saghir (Schneider Electric) | Rajesh D. Sharma (Schneider Electric)
- Document ID
- Society of Petroleum Engineers
- SPE Middle East Artificial Lift Conference and Exhibition, 28-29 November, Manama, Bahrain
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 2.1.3 Completion Equipment, 7.6.6 Artificial Intelligence, 7.2.1 Risk, Uncertainty and Risk Assessment, 3.1.1 Beam and related pumping techniques, 7 Management and Information, 7.2 Risk Management and Decision-Making, 3.1 Artificial Lift Systems, 3 Production and Well Operations
- Edge Analytics, Artificial Lift, IIoT, Machine Learning, Artificial Intelligence
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Oil and Gas operators now have the possibility to collect and leverage significant amounts of data directly at the extremities of their production networks. Data combined with Industrial Internet of Things (IIoT) architecture is an opportunity to improve maintenance of assets, increase their up-time, reduce safety risks and optimize operational costs. However, to turn data into meaningful insights, Oil and Gas industry needs to fully take benefit of Machine Learning (ML) models which are able to consume real-time data and provide insights in isolated locations with scarce connectivity. These ML models need to be precise, robust and compatible with Edge computing capabilities.
This paper presents an analytics solution for rod pumps, capable of automated Dynagraph Card recognition at the wellhead leveraging an ensemble of ML models deployed at the Edge. The proposed solution does not require Internet connectivity to generate alarms and addresses confidentiality requirements of Oil and Gas industry. An overview of the employed ML models as well as the computing and communication infrastructure is given. We believe the given outline is insightful for the petroleum industry on its road to digitization and optimization of Artificial Lift systems.
|File Size||1 MB||Number of Pages||14|
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