# A Nodal Analysis Based Monitoring of an Electric Submersible Pump Operation in Multiphase Flow
## 3 ESP Fault Monitoring and Diagnosis Review  
As discussed in the previous section, operating the ESP in the free gas environment results in an abnormal operation, and damages the ESP components. Unfortunately, it is impossible to limit free gas formation in the production well; however, there are several ways to control its liberation from the produced fluid. [PERSON] [5] demonstrated that increasing operating PIP reduces the free gas amount liberated from the produced fluid solution. Still, this approach impedes the oil production rate. Additional efforts have been subjected to ESP monitoring and diagnosis to overcome operation losses, proceed with the prevention measures, and promote abrupt free gas formation since removing and replacing a damaged pump is 40-fold more expensive than installing a new pump [22]. This section presents different perspectives for monitoring and diagnosing ESP abnormal conditions. [PERSON] and Germany [23] demonstrated the usage of artificial intelligence (AI) technology to detect, diagnose, and monitor ESP abnormal behavior. They explained how the combination of artificial intelligence (AI) and Internet of Things (IoT) technologies provide an effective way to use data-driven analytics to detect the ESP abnormal behavior, based on the downhole data and providing decision support for managing the roots of the abnormal conditions. Using the field data of the 30 ESPs, they showed how the AI could provide a solution to the production disruptions caused by the several ESP failures. Unfortunately, their approach focused on the data analysis of the previous scenario to avoid future failure; this is hard to adopt in the green field where the previous failure data are not available. [PERSON] [24] showed the application of real-time data processing and interpretation to reduce ESP failure. The author highlighted that a combination of the reservoir and production data results in the effective management of the ESP well. It also enhanced the oil well and reservoir monitoring, improving surveillance, analysis, and diagnosis of the ESP well. As a result, the ESP well operation was optimized, which reduced the failure likelihood. Through the presented case study (Brownfield of a Permian Basin), this approach optimized the ESP well production through downhole equipment failure reduction, early-warning detections, and reduced the equipment workover. Similar to the previous approach, it is hard to adopt it where the historical failure data are not present. Moreover, [PERSON] et al. [25] employed pattern recognition analysis for ESP well surveillance. The authors developed an ESP abnormal troubleshooting checklist that quickly identifies and differentiates the root of ESP malfunction. This approach can be adapted to the new field by employing the developed checklist to diagnose ESP abnormal conditions. However, a checklist requires more attention because different failures might have the same signature; thus, ESP diagnosis can be compromised. [PERSON] et al. [26] reported that proactive ESP monitoring was possible via real-time monitoring and diagnosis. They showed that monitoring real-time data of downhole sensor readings and surface data provides a diagnostic tool that identifies ESP abnormality that could lead to shutdowns; thus, ESP trips can be prevented in advance. [PERSON] et al. [27] used ESP data to monitor ESP performance, diagnose issues, and optimize production during polymer flooding. The authors combined a real-time ESP parameter alarm system, time-lapse analysis (production tubing pressure drop, pump head de-rating factor, pump, and VFD horsepower), deadhead, and multi choke test data to evaluate the most likely mechanisms leading to ESP abnormality. Their study provided a diagnostic approach to differentiate the polymer flooding plugging or gas-related failure. Based on their findings, the approach proposed can be applied in the new oil well to optimize and monitor ESP operating conditions. [PERSON] et al. [28] developed an ESP troubleshooting guide that reduced failures.