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

Fahimeh Hadavimoghaddam, Mehdi Ostadhassan, Ehsan Heidaryan, Mohammad Ali Sadri, Inna Chapanova, Evgeny Popov, Alexey Cheremisin and Saeed Rafieepour
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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

Energies, 2021, vol. 14, issue 4, 1-16

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 ( ? 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.

Keywords: viscosity; PVT properties; dead oil viscosity; machine learning; SuperLearner (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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