Guest Editorial: Artificial Intelligence Can Reduce ESP Failures

Abstract

Guest editorial Electrical submersible pump (ESP) technology predominates the artificial lift options available to onshore and off-shore operators for maximizing production from medium-to-deep reservoirs. Although designed, engineered, and built to withstand extreme subsurface conditions—corrosive liquids, scalding temperatures, and intense pressures—ESPs can and do fail without warning, often despite having monitoring systems in place. These failures disrupt production and operator cash flows. Ultimately, the costs of replacing an ESP and its associated production losses can be enormous. But the risk of ESP failures can be greatly reduced with the right combination of advanced technologies, such as applying artificial intelligence (AI) combined with a secure, cloud-based Internet of Things (IoT) autonomous surveillance system. This provides operators with an early-warning system of ESP performance degradation in the form of a probabilistic, predictive maintenance model. With it, they can make better-informed decisions about the root causes of performance anomalies as well as how to mitigate, remediate, or manage them until the well’s next planned shutdown. In one pilot deployment of this application across a fleet of 30 ESPs, the failure of one was accurately predicted 12 days before it occurred. Monitoring of ESP Fleets Today, ESPs are deployed into reservoirs with sufficient sensors and instrumentation to enable continuous alarm-limit monitoring by technicians, who also keep a close watch on above-ground controls and motor drives. A distributed control system, often a supervisory control and data acquisition (SCADA) system, transmits an ESP’s operating data, which is recorded in a historian database. The data can then be used later for diagnostics. In practice, this approach is most often reactive, not proactive. That’s because conventional tools lack the ability to predict an ESP failure. In contrast, consider the use of a more predictive monitoring model that can employ AI-based pattern recognition. This capability not only can identify ESP behavioral anomalies but also can provide actionable intelligence about their root causes—and, importantly, the probability of an ESP failure based on the data-fueled refinement of the AI algorithms (i.e., machine learning). With this knowledge, an operator’s technical staff can better decide a proper course of action should they get an early warning about ESP performance issues. They can also be freed from having to constantly monitor ESP systems for alarm notifications and implement corrective actions. One benefit of this is that their time can be invested in more value-adding production activities. As an implementation of AI, machine learning (ML) is based on the use of deep learning via neural networks. To implement it as an ESP predictive monitoring model, data scientists use ESP historical data to create a training data set. The latter then “teaches” the software-coded neural network all the highly dynamic, relational behaviors and interactions between the many variables within a process, as defined by an ESP’s various operating KPIs.