Prediction method of fluid production profiles based on a probabilistic modeling method

The traditional fluid production splitting method cannot consider the influences of interzonal interference, injection wells and adjacent wells, so it is difficult to precisely assess the actual downhole conditions. Meanwhile, due to high cost of production profile testing in offshore oilfields, the conventional machine learning methods also face the problem of small sampling numbers, which has a great limitation for their application. In this study, a hybrid learning model was proposed with Bayesian neural network and extreme gradient boosting algorithm, which can formulate a more robust model based on less data. By combining the neural network with probabilistic modeling, mining the distribution characteristics of stratified liquid production data and analyzing the main control factors, the hybrid learning algorithm can accurately predict the liquid production in different layers. The new method was applied to prediction of the liquid production profiles in a real oilfield in order to verify its effectiveness. The results show that, compared with the KH splitting method, the splitting coefficient can be fixed in the calculation and does not fluctuate with the production process. The proposed method can learn from the historical data, with an accuracy of 87. 9%, and the predicted results are closer to the real liquid production of each layer. © 2024 University of Petroleum, China. All rights reserved.