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Artificial Neural Network Surrogate Modeling of Oil Reservoir: A Case Study

  • Oleg Sudakov
  • Dmitri Koroteev
  • Boris Belozerov
  • Evgeny BurnaevEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11555)

Abstract

We develop a data-driven model, introducing recent advances in machine learning to reservoir simulation. We use a conventional reservoir modeling tool to generate training set and a special ensemble of artificial neural networks (ANNs) to build a predictive model. The ANN-based model allows to reproduce the time dependence of fluids and pressure distribution within the computational cells of the reservoir model. We compare the performance of the ANN-based model with conventional reservoir modeling and illustrate that ANN-based model (1) is able to capture all the output parameters of the conventional model with very high accuracy and (2) demonstrate much higher computational performance. We finally elaborate on further options for research and developments within the area of reservoir modeling.

Keywords

Reservoir modeling Machine learning Surrogate modeling Artificial neural networks 

Notes

Acknowledgements

The work was supported by the Russian Science Foundation under Grant 19-41-04109.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Oleg Sudakov
    • 1
  • Dmitri Koroteev
    • 1
  • Boris Belozerov
    • 2
  • Evgeny Burnaev
    • 1
    Email author
  1. 1.Skolkovo Institute of Science and TechnologyMoscowRussia
  2. 2.Gazprom Neft, Science and Technology CenterSt. PetersburgRussia

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