Application of Neural Network for Two-Phase Flow through Chokes

Abstract

Abstract This study shows the utilization of the Artificial Neural Network (ANN) as a practical engineering tool for estimating the flow rate and selecting the optimal choke size. In this study, the existing choke correlations available in the literature were reviewed, evaluated and compared with the newly derived ANN. The new method can be used to predict the required choke size and can also be used to provide a quick and accurate evaluation of the well performance, by considering wellhead conditions and pressure-volume-temperature (PVT) parameters. Two models were developed based on 4,031 data points: 80% for training, 10% for validation and 10% for testing. The new models were found to outperform all the existing correlations and have provided the lowest error, with an average absolute percent error of 3.7% for the choke size prediction and 6.7% for the flow rate estimation. The new models can estimate with a higher accuracy the optimal choke size and flow rate. Therefore, the new models can help advance reservoir management and production operations in the following ways: producing the reservoir at the optimal rate; preventing water or gas coning; maintaining back pressure; and protecting formation and surface equipment from unusual pressure fluctuation.