Replacement of numerical simulations with machine learning in the inverse problem of two-phase flow in porous medium

A possibility to replace full-physics numerical simulations with machine-learning-based algorithms in inverse problems of multiphase flow in porous media is studied on an example of specialized oil-well tests. The performance function is computed at each iteration of the inverse problem solution usi...

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Bibliographic Details
Published in:Journal of physics. Conference series 2019-11, Vol.1391 (1), p.12146
Main Authors: Goncharova, Yu A, Indrupskiy, I M
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Computer simulation
Inverse problems
Iterative methods
Learning theory
Machine learning
Mathematical analysis
Multiphase flow
Physics
Porous media
Simulation
Two phase flow
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Summary:A possibility to replace full-physics numerical simulations with machine-learning-based algorithms in inverse problems of multiphase flow in porous media is studied on an example of specialized oil-well tests. The performance function is computed at each iteration of the inverse problem solution using an artificial neural network (ANN) instead of forward numerical simulations. Proper ANN structure, training set, and learning algorithm are established and implemented. Achieved approximation quality for the performance function on a test sample is well suitable for the inverse problem solution. Further improvements and alternative ANN formulations are proposed for practical applications.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1391/1/012146