Dynamometer-Card Classification Uses Machine Learning

Abstract

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 The paper has not been peer reviewed. 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. Data Collection Sucker-rod pumps are widely deployed in the Bahrain Field. There are two different types of communication layers used across the field, radio and optical fiber. Almost 300 optical-fiber- connected beam-pump units (BPUs) have been selected for surveillance and advanced analysis. Fiber is used to avoid any communication issues during data collection for the AI project. The sampling rate of collection for each pump is one reading per 20 seconds, thus covering three pump cards per minute. All BPUs are connected to a central server that converts hardware-communication protocol used by a programmable logic controller into the open-platform-communication (OPC) protocol. Fig. 1 shows the details of the 209 values representing a single pump card being read from a BPU and stored in the operator database. During the data-collection period, a total of 5,380,163 pump cards from 297 BPUs were collected and stored in the database. Data Labeling and Preparation In machine learning, an important step after data collection is data labeling, which is usually performed manually by experts. The labeled data set is used to feed machine-learning algorithms that detect patterns specific to each class. A total of 35,292 pump cards has been labeled (more than 1,000 cards per day on average). A useful feature of the labeling software is that it allows labeling an entire time period of cards in one shot, although such labeling should be performed with care.