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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(γAPI,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.

The role of thermal petrophysics in modelling basin and petroleum systems has been growing in recent years for at least three major reasons. First, the results of deep continental scientific drilling have led to dramatic changes in the understanding of the thermal regime at substantial depths in the interior of the Earth (Emmerman & Lauterjung, 1997; Popov, Popov, Chekhonin, Spasennykh, & Goncharov, 2019). Second, temperature is a key parameter for determining the transformation of kerogen into oil and gas, and its evaluation is impossible without reliable data on thermal properties. The most sophisticated and appropriate modelling techniques for analysing thermal histories and organic maturation levels may fail when applied to real basins if the thermal conductivity

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