Analytics Solution Helps Identify Rod-Pump Failure at the Wellhead
- Chris Carpenter (JPT Technology Editor)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- May 2019
- Document Type
- Journal Paper
- 63 - 64
- 2018. Society of Petroleum Engineers
- 6 in the last 30 days
- 93 since 2007
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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 192513, “Industrial Internet of Things Edge Analytics: Deploying Machine Learning at the Wellhead To Identify Rod-Pump Failure,” by Bartosz Boguslawski, Matthieu Boujonnier, Loryne Bissuel-Beauvais, Fahd Saghir, SPE, and Rajesh D. Sharma, SPE, Schneider Electric, prepared for the 2018 SPE Middle East Artificial Lift Conference and Exhibition, Manama, Bahrain, 28–29 November. The paper has not been peer reviewed.
Industrial-Internet-of-things (IIOT) architecture provides an opportunity to improve asset uptime and maintenance of assets, reduce safety risks, and optimize operational costs. However, to turn data into meaningful insights, the industry must make full benefit of machine-learning (ML) models. This paper presents an analytics solution for identifying rod-pump failure capable of automated dynacard recognition at the wellhead that uses an ensemble of ML models. The proposed solution does not require Internet connectivity to generate alarms and meets confidentiality requirements.
Rod-Pump-Control (RPC) Architecture
Thanks to recent progress in microelectronics, the embedding of ML models in remote places with scarce connectivity, known as edge computing, is possible. Thanks to the insights generated at the oil field, onsite maintenance teams can apply immediate corrective responses, working efficiently and safely.
RPC architecture enables automated control of sucker-rod pumps. A variable-speed drive (VSD) controls the pump by adjusting the speed of the motor to downhole conditions. The VSD is controlled by a remote terminal unit (RTU) that provides speed reference for improved rod-pump control. The RTU also collects sensor measurements. In the work covered in the paper, two sensors, namely the proximity sensor and the load cell, are of particular interest. The proximity sensor is mounted near the crank arm, while the load cell is a transducer mounted between the polished rod clamp and the bridle. The RTU sends the measurements to a touchscreen human/machine interface for a local user. It also communicates the measurements to a supervisory control and data-acquisition (SCADA) system. The communication between the RTU and the SCADA host is established through external wireless communication devices such as radios or cellular modems. Furthermore, in the ex-ample discussed in the paper, a local edge computing gateway is added to the architecture. Edge gateways have significant computing power and can run ML-based applications. The gateway retrieves the data from the RTU and performs onsite analytics, generates alarms, and communicates with the cloud sporadically.
Using the measurements from the two sensors, the RPC produces a surface card (or dynacard), from which downhole pump conditions may be deduced, and consequently calculates a downhole card, from which downhole pump conditions may be inferred. The downhole card is, in essence, a translated surface card with oscillatory harmonics removed. An experienced operator is able to infer from the downhole card whether a pump is operating normally or has failed in some way.
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