Artificial Neural Network Model to Predict Production Rate of Electrical Submersible Pump Wells

Abstract

Summary Production data are essential for designing and operating electrical submersible pump (ESP) systems. This study aims to develop artificial neural network (ANN) models to predict flow rates of ESP artificially lifted wells. The ANN models were developed using 31,652 data points randomly split into 80% (25,744 data points) for training and 20% (5,625 data points) for testing. Each data set included measurements for wellhead parameters, fluid properties, ESP downhole sensor measurements, and variable speed drive (VSD) sensors parameters. The models consisted of four separate neural networks to predict oil, water, gas, and liquid flow rates. Sensitivity analyses were performed to determine the optimum number of input parameters that can be used in the model. The best performance was achieved with ANN models of 16 input parameters that are readily available in ESP wells. The results of the best ANN configuration indicate that the mean absolute percent error (MAPE) between the predicted flow rates and the actual measurements for the testing data points of the oil, water, gas, and liquid networks is 3.7, 5.2, 6.4, and 4.1%, respectively. In addition, the correlation coefficients (R2) are 0.991, 0.992, 0.983, and 0.979 for the estimated oil, water, gas, and liquid flow rates for the testing data points, respectively. The performance of the ANN models was compared against performance of published physics-based models and the results were comparable. Unlike the physics-based methods, the ANN models have the advantage that they do not require periodic calibration. The ANN models were used to predict the flow rate curves of an oilwell in the Western Desert of Egypt. The results were compared to the actual separator test data. It was clear that the model results matched the actual test data. The ANN model is useful for predicting individual well production rates within wide variety of pumping conditions and completion configurations. This should allow for continuous monitoring, optimization, and performance analysis of ESP wells as well as quicker response to operational issues. In comparison to traditional separators and multiphase flowmeters (MPFMs), the use of the developed ANN models is simple, quick, and inexpensive.

Real-Time Liquid Rate and Water Cut Prediction From the Electrical Submersible Pump Sensors Data Using Machine-Learning Algorithms

“…This leads to another challenge in developing data-driven models, which is the lack of transparency and interpretability of some algorithms. From the data-driven applications studied in the literature, , we have concluded that their workflow starts with the usage of more complex black-box algorithms without applying more interpretable machine learning algorithms. Therefore, the proposed methodology comprises three modeling steps: symbolic regression, XGBoosting, and deep learning.…”