Physics-assisted cascade neural network model for predicting pressure losses of a three-phase mixture in a pipeline

Shlykova A.O., Shevchenko Y.A., Minin S.V., Koroleva A.P.
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The paper presents a cascade model of a physically supported neural network designed to predict pressure drop in three-phase flow (oil, gas, water) in a pipe section with various angles of inclination. To overcome the constraints of existing empirical correlations and computation-intensive numerical modeling methods, we propose an architecture that decomposes the problem into three sequential physically interpretable subtasks: regression prediction of the fluid hold-up coefficient, fluid flow regime classification, and pressure gradient evaluation. Each subtask is solved by a separate fully connected neural network, the output of which is passed to the next model in the cascade. Training and testing of the proposed architecture was performed on an extensive synthetic dataset (8 · 107 records) generated using a semi-empirical model. Verification is performed on independent experimental data. A comparative analysis with a single fully connected (non-cascade) neural network is made, and the sensitivity of the models is examined using Sobol and Borgonovo methods. The cascade model demonstrates superior accuracy and ensures high interpretability of results by providing intermediate physical parameters (fluid hold-up coefficient, flow regime). The developed model has low computational complexity, which allows it to be used in real-time systems and digital twins of hydraulic systems in the oil and gas industry.

Keywords: physically assisted neural network, multiphase flow, machine learning, flow behavior, regression model
Citation in English: Shlykova A.O., Shevchenko Y.A., Minin S.V., Koroleva A.P. Physics-assisted cascade neural network model for predicting pressure losses of a three-phase mixture in a pipeline // Computer Research and Modeling, 2026, vol. 18, no. 1, pp. 117-131
Citation in English: Shlykova A.O., Shevchenko Y.A., Minin S.V., Koroleva A.P. Physics-assisted cascade neural network model for predicting pressure losses of a three-phase mixture in a pipeline // Computer Research and Modeling, 2026, vol. 18, no. 1, pp. 117-131
DOI: 10.20537/2076-7633-2026-18-1-117-131
URL: http://crm-en.ics.org.ru/journal/article/3691/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

Copyright © 2026 Shlykova A.O., Shevchenko Y.A., Minin S.V., Koroleva A.P.

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