Beam Pump Dynamometer Card Classification Using Machine Learning

S. Sharaf, P. Bangert, Mohamed Fardan, Khalil Alqassab, M. Abubakr, Mahmood Ahmed
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引用次数: 5

Abstract

A beam or a sucker rod pump is an artificial-lift pumping system using a surface power source to drive a downhole pump assembly. A beam and crank assembly creates reciprocating motion in a sucker-rod string that connects to the downhole pump assembly. The pump contains a plunger and valve assembly to convert the reciprocating motion to vertical fluid movement. A dynamometer is a diagnostic device used on sucker rod pumped wells that measures the load on the top rod and plots this load in relation to the polished rod position as the pumping unit moves through each stroke cycle. The analysis of the dynamometer card data delivers valuable insights on the status of the pump and indicates if future actions are required. In practice, the load versus displacement plot shape can be visually categorized in different classes where each shape has a specific meaning and indicates certain operating conditions. Machine learning algorithms are computing systems that learn to perform tasks by considering examples, generally without being programmed with any task-specific rules. During a period of approximately two (2) months, we collected 5,380,163 different cards from 297 beam pumps deployed in the Bahrain Field using the Supervisory Control and Data Acquisition (SCADA) system with an Open Platform Communication (OPC) interface. 35,292 cards are manually labelled by experts into twelve (12) classes. The dataset is split into 80% training and 20% holdout datasets. A training dataset is split into 5-fold cross validation. Different machine learning algorithms are evaluated predicting pump card class and their performance is compared. The top performing model, Gradient Boosting Machines (GBM) Classifier, achieves 99.98% accuracy in cross validation and 100% accuracy on holdout dataset without any extensive feature engineering. This paper explains the steps taken to improve surveillance of beam pumps using dynamometer card data and machine learning techniques and the lessons learned from executing the first Artificial Intelligence (AI) project within Tatweer Petroleum.
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使用机器学习的束泵测功机卡片分类
有杆泵是一种人工举升泵系统,使用地面电源驱动井下泵组件。梁和曲柄组件在连接到井下泵组件的抽油杆柱中产生往复运动。该泵包含柱塞和阀组件,用于将往复运动转换为垂直流体运动。测力计是一种用于有杆抽油泵井的诊断设备,它可以测量顶杆上的载荷,并在抽油机在每个冲程周期内移动时绘制出与磨光杆位置相关的载荷图。对测功卡数据的分析提供了对泵的状态有价值的见解,并指示是否需要采取后续行动。实际上,载荷与位移图形状可以直观地分为不同的类别,其中每个形状都有特定的含义,并指示某些操作条件。机器学习算法是通过考虑示例来学习执行任务的计算系统,通常不需要使用任何特定于任务的规则进行编程。在大约两(2)个月的时间里,我们使用带有开放平台通信(OPC)接口的监控和数据采集(SCADA)系统,从部署在巴林油田的297台束流泵中收集了5,380,163张不同的卡。35,292张卡片由专家手工标记为12类。数据集分为80%的训练数据集和20%的保留数据集。训练数据集被分成5次交叉验证。评估了不同的机器学习算法预测泵卡类,并比较了它们的性能。表现最好的模型是Gradient Boosting Machines (GBM) Classifier,它在交叉验证中达到99.98%的准确率,在holdout数据集上达到100%的准确率,而不需要任何广泛的特征工程。本文介绍了利用测功卡数据和机器学习技术改善束流泵监测的步骤,以及在Tatweer石油公司执行第一个人工智能(AI)项目的经验教训。
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