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

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(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.

An assessment of ensemble learning approaches and single-based machine learning algorithms for the characterization of undersaturated oil viscosity

“…When combined with other single-based algorithm in voting, significantly rose with R 2 values (0.73399, 0.87540, 0.88078, 0.88015 and 0.92915) for each of the combinations in voting approach. Figures 6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26 The non-fitness of the points on the 450 line reveals a poor agreement between the measured values and predicted values. Hence, for this dataset, we can conclude that those respective models are weak and unreliable.…”

“…Four parameters influenced this relationship: the gas-to-oil ratio, temperature and the gravity of both oil and gas. Many more correlations were reported for other crude samples based on the same characteristics, with more experimental data used in general than in Standing's study [8][9][10][11][12][13][14][15][16].…”

Machine-Learning Methods for Complex Flows

“…The fourth article, "Prediction of dead oil viscosity: machine learning vs. classical correlations", was written by Hadavimoghaddam et al [34]. In this article they deal with the modelling of the dead-oil viscosity, which is a very important parameter in the context of reservoir-engineering problems.…”

Comparison of Empirical Models to Predict Viscosity of Secondary Vacuum Gas Oils

“…The empirical methods include a wide variety of equations used throughout the industry involving constants calculated from experimental data by regression. Machine learning (ML) and artificial intelligence techniques (AI) have also been used to improve the prediction of oil viscosity [27][28][29][30][31][32]. The literature argues that the lowest average absolute relative error can be achieved when viscosity is predicted by AI models and the highest correlation coefficient as compared to existing empirical correlations [27,32].…”

“…Machine learning (ML) and artificial intelligence techniques (AI) have also been used to improve the prediction of oil viscosity [27][28][29][30][31][32]. The literature argues that the lowest average absolute relative error can be achieved when viscosity is predicted by AI models and the highest correlation coefficient as compared to existing empirical correlations [27,32]. The empirical models are typically simpler and only require a few basic parameters such as mid-boiling point temperature and specific gravity of the oil, or alternatively a single viscosity measurement, in order to extrapolate the viscosity to a different temperature [20].…”