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Using Machine Learning-Based Predictive Models to Enable Preventative Maintenance and Prevent ESP Downtime

机译：使用基于机器学习的预测模型来实现预防性维护并防止esp停机时间

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This paper focuses on the use of artificial intelligence (AI) and machine learning (ML) algorithms to implement anomaly detection and shows how this concept can be extended to implement autonomous well surveillance. Today, critical equipment is monitored by implementing automation and control systems with built-in protection logic for safe operation of equipment and for shutdown of the system in the event that operation deviates outside of valid process conditions. These automation and control systems require constant surveillance by a human operator to verify that all processes are running normally. It is the human operator's responsibility to react to any alarm conditions that occur during operation. Often, the alarm trigger event occurs without early notification and the operator has a short period of time to react. This way of controlling operations requires skilled operators with a great deal of experience to monitor and control the system in an effective manner. It also limits the amount of time the operator can allocate toward optimization. Autonomous surveillance is the concept of training an AI system to provide early detection of abnormal behavior. In this way, the system can take over the task of constant surveillance of process operation, leaving the experienced human operator with additional capacity to focus his time on more productive actions. For example, the human operator can use a combination of process data and decision support information from the AI system to consider current operating conditions and implement a more optimized setpoint, which could increase production or extend equipment life expectancy. In this paper, an example is presented, which is based on monitoring electric submersible pumps (ESPs) using a deep learning neural network that was trained on historical data from the process control system historian. In addition to outlining the benefits that AI-assisted surveillance provides when compared to conventional methods, the paper provides a system architecture blueprint for implementing an autonomous monitoring application across different types of ESP fleets by connecting sensor data directly to a cloud-based monitoring system. The paper builds on the work of previous SPE papers by providing up-to-date results of a pilot project, where a predictive maintenance model has been running for 10 months. On the project, 30 ESPs ranging in power from as low as 200-kW to as high as 500-kW were deployed and monitored using an AI-supported predictive maintenance model. To date, the results have been extremely positive. In one case, an ESP interruption was predicted by the application 12 days before the actual failure occurred. Following several months of testing/use of the ML-based predictive maintenance solution during the pilot deployment, the ESP fleet operator concluded that the system can detect multiple kinds of anomalies in advance, even previously unknown ones. This capability is a significant distinction between the AI-based model and conventional models employed by other ESP diagnostic tools. Although these new, unknown types of complex ESP operational anomalies were difficult to interpret as to their root causes, they could still have led to ESP performance degradation and possible failure nonetheless, if not mitigated or remediated.
机译：本文侧重于使用人工智能（AI）和机器学习（ML）算法来实现异常检测，并展示如何扩展该概念以实现自主井监视。如今，通过实施具有内置保护逻辑的自动化和控制系统来监测关键设备，以便在操作偏离有效的过程条件之外，通过内置保护逻辑和用于关闭系统的关闭。这些自动化和控制系统需要由人工操作员持续监视，以验证所有进程是否正常运行。人类运营商有责任对操作期间发生的任何警报条件作出反应。通常，警报触发事件发生而没有早期通知，并且操作员在短时间内进行反应。这种控制操作的方式需要熟练的运营商以有效的方式监视和控制系统的大量经验。它还限制了操作员可以分配朝向优化的时间量。自主监督是培训AI系统的概念，以提供异常行为的早期检测。通过这种方式，系统可以接管过程运行不断监视的任务，使经验丰富的人类运营商具有额外的能力来将他的时间集中在更高效的行动上。例如，人类运营商可以使用来自AI系统的过程数据和决策支持信息的组合来考虑当前的操作条件并实现更优化的设定点，这可能会增加生产或延长设备寿命的预期寿命。在本文中，提出了一个示例，该示例是使用深入学习神经网络监控电动潜水泵（ESP），该网络神经网络从过程控制系统历史学家培训。除了概述与传统方法相比提供AI辅助监视提供的好处之外，本文还提供了一种系统架构蓝图，用于通过将传感器数据直接连接到基于云的监控系统来实现不同类型的ESP车队的自主监测应用。本文通过提供试点项目的最新结果，建立了先前的SPE文件的工作，其中预测维护模型已经运行了10个月。在项目上，使用AI支持的预测维护模型部署和监控30个ESP从低至200kW的电源到高达500 kW的电源。迄今为止，结果非常积极。在一个情况下，应用程序在发生实际失败之前预测了ESP中断。在试点部署期间的几个月的测试/使用基于ML的预测性维护解决方案之后，ESP舰队操作员得出结论，该系统可以提前检测多种异常，甚至是先前未知的。这种能力是基于AI的模型和其他ESP诊断工具所采用的传统模型之间的显着区别。虽然这些新的，未知类型的复杂的esp运营异常难以解释其根本原因，但如果没有减轻或修复，它们仍然可能导致ESP性能下降和可能的失败。

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