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Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Author

Listed:
  • Fahimeh Hadavimoghaddam

    (Department of Oil Field Development and Operation, Faculty of Oil and Gas Field Development, 119991 Moscow, Russia)

  • Mehdi Ostadhassan

    (Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Ministry of Education, Northeast Petroleum University, Daqing 163318, China)

  • Ehsan Heidaryan

    (Department of Chemical Engineering, Engineering School, University of São Paulo (USP), Caixa Postal 61548, São Paulo 05424-970, Brazil)

  • Mohammad Ali Sadri

    (Skolkovo Institute of Science and Technology (Skoltech), 143026 Moscow, Russia)

  • Inna Chapanova

    (Skolkovo Institute of Science and Technology (Skoltech), 143026 Moscow, Russia)

  • Evgeny Popov

    (Skolkovo Institute of Science and Technology (Skoltech), 143026 Moscow, Russia)

  • Alexey Cheremisin

    (Skolkovo Institute of Science and Technology (Skoltech), 143026 Moscow, Russia)

  • Saeed Rafieepour

    (McDougall School of Petroleum Engineering, University of Tulsa, Tulsa, OK 74110, USA)

Abstract

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f ( γ A P I , T ) , has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

Suggested Citation

  • Fahimeh Hadavimoghaddam & Mehdi Ostadhassan & Ehsan Heidaryan & Mohammad Ali Sadri & Inna Chapanova & Evgeny Popov & Alexey Cheremisin & Saeed Rafieepour, 2021. "Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations," Energies, MDPI, vol. 14(4), pages 1-16, February.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:930-:d:497223
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    References listed on IDEAS

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    Cited by:

    1. Ricardo Vinuesa & Soledad Le Clainche, 2022. "Machine-Learning Methods for Complex Flows," Energies, MDPI, vol. 15(4), pages 1-5, February.
    2. Dicho S. Stratiev & Svetoslav Nenov & Ivelina K. Shishkova & Rosen K. Dinkov & Kamen Zlatanov & Dobromir Yordanov & Sotir Sotirov & Evdokia Sotirova & Vassia Atanassova & Krassimir Atanassov & Danail , 2021. "Comparison of Empirical Models to Predict Viscosity of Secondary Vacuum Gas Oils," Resources, MDPI, vol. 10(8), pages 1-17, August.

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