This paper presents a novel approach using artificial neural networks (ANNs) to predict the discharge pressure of electrical submersible pumps (ESPs). A data set of more than 12,000 data points collected from 40 different wells was used to train and test various ANN models with different input parameters. The ability to predict discharge pressure accurately can lead to the early detection of possible anomalies.
Methodology
Data Collection. The data-collection process for ESP sensor data involved collecting data from 40 wells operational during 2019–2023 with starting dates from 2019 through 2022. The data included key parameters provided in the complete paper.
Data Cleaning and Integration. The data-cleaning process was performed to ensure that the accuracy and reliability of the data were maintained. Scatter plots were used to visualize the distribution of each variable in the data set, and outliers were identified and removed or smoothed. Missing values were addressed by fill-in using statistical methods or by removing them from the data set if the percentage of missing values was significant.
