The Approach to Evaluate The Confidence of Flow Rate Prediction Accuracy in The Tasks of Virtual Flow Metering

Summary The paper is devoted to computation of the prediction interval and evaluation of regression accuracy, applied for flowrate computation with virtual flowmeters. Our approach is based on ensembles of neural networks known as Mixture Density Networks and minimizing of the negative-log likelihood function. We investigated the advantages of the applied method to calculate the oil rates and prediction interval using synthetic dataset consisting of 180 wells. The approach has demonstrated to be robust and sensitive the presence of signals variability and noise impact, and to the error caused by the model's uncertainty caused by statistical difference between training and testing datasets.