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Dynamometer-Card Classification Uses Machine Learning

Topics: Artificial lift

In reciprocating rod pumping systems, analysis of dynamometer-card data can deliver valuable insight into the status of the pump, and can indicate if future action is required. The complete paper explains the steps taken to improve surveillance of beam pumps using dynamometer-card data and machine-learning techniques, and reviews lessons learned from executing the operator’s first artificial intelligence (AI) project.

Background

The oldest and most widely used method of artificial lift is called beam pumping, or the sucker-rod lift method. A dynamometer is a device used on beam pumps that measures load on the polished rod (top) and plots the load in relation to the rod position as the pumping unit moves through a stroke cycle. This plot is known as the surface card.

A pump card is a plot of load vs. position on the pump’s plunger. The pump card is more useful for surveillance purposes because it filters effects of anything above the plunger and provides standard pump card shapes for interpreting pump operating conditions. Identification and diagnosis of beam pumps using the pump card is an expensive human visual-interpretation process, not only requiring significant labor time but also deep expertise in the domain.

Use of machine-learning techniques for pattern recognition can help automate the visual interpretation process, increasing efficiency and reducing maintenance activities resulting from missed early diagnosis.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 194949, “Beam-Pump Dynamometer Card Classification Using Machine Learning,” by Sayed Ali Sharaf, Tatweer Petroleum; Patrick Bangert, SPE, Algorithmica Technologies; and Mohamed Fardan, Tatweer Petroleum, et al., prepared for the 2019 SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain, 18–21 March. The paper has not been peer reviewed.
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Dynamometer-Card Classification Uses Machine Learning

01 March 2020

Volume: 72 | Issue: 3

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