# A Nodal Analysis Based Monitoring of an Electric Submersible Pump Operation in Multiphase Flow
## 3 ESP Fault Monitoring and Diagnosis Review
Using monitoring information, tearing down evidence, setting configuration, and building a database with all the information of 450 oil well data, they developed a troubleshooting manual, which quickly identifies likely conditions that impede the production and performance of ESP systems. In addition, [PERSON] et al. [1] demonstrated that significant ESP failure happens when its key parameters start deviating from the normal operating range until an ESP stops working. They proposed an application of the principal component analysis (PCA) model to perform ESP fault detection to maintain ESP in the normal operational range. [PERSON] et al. [29] showed the machine learning algorithm's preference for future failure prediction based on historical ESP data. They adopted Classification and Regression Trees (CART) to detect and classify elementary faults in the ESP well system. CART's proposed methodology monitored the system based on the pressure variation, flow rate, torque, and temperature. [PERSON] et al. [30] proposed a new downhole monitoring tool, which uses an experimental phase to neutral communication scheme. In their design, an effective signal communication path is formed by the external impedance on the wellhead (connecting one phase) to the grounding ESP motor. The laboratory and field testing demonstrated the reliability of the method. [PERSON] et al. [31] proposed an online ESP fault diagnosis that combines the ESP electrical and production parameters. They designed the algorithms that perform feature extraction, combining the electrical and production parameters. This approach effectively monitored the nine typical operating fault conditions at reduced downhole sensor impact. [PERSON] et al. [32] presented the three-phase optical voltage sensor for ESP monitoring. They applied a piezoelectric transducer that converts the voltage into strain. The strain is measured using a fiber Bragg grating sensor. The measured strain is amplified at the grating location and improves the measurement resolution. The electrical breakdown was minimized using the finite element electrostatic modeling software. This approach improved the electrical performance in the ESP well system. [PERSON] [33] employed a nodal, analytical, graphical method to monitor ESP well performance. He provided a general approach to assessing the ESP-assisted well based on the pump intake and discharge pressure deviation trends. However, these approaches afford diagnostic tools after abnormal conditions have occurred and provide only retrospective explanations of real-world failures. It is essential to predict and prevent ESP failure associated with a drastic performance reduction and other problems. An effective early-warning system must be designed before ESP installation. We use Schlumberger PIPESIM software (version 2017.01) to predict ESP operation pre-installation. We consider the uncertainty imparted by multiphase well conditions. We monitor the PIP, Pd, and production rate; we compare these values (at all times) to normal reference values. Any difference predicts abnormal ESP operation. The work is novel in that we devise reference threshold values for several distinct phases of ESP operation. We used an oil well located in the Delaware Basin to evaluate the model. To generalize the model, we evaluated different types of wells. For example, an ESP deployed in a dead well was used to stimulate production; an ESP installed in a naturally flowing well served to increase production. We consider both single-phase and multiphase conditions inside the pump.