New correlations for prediction of viscosity and density of Egyptian oil reservoirs

El-hoshoudy, A.N.; Farag, A.B.; Ali, Omnia; El-Batanoney, M. H.; Desouky, Said E.; Ramzi, M.

doi:10.1016/j.fuel.2013.05.045

Cited by 24 publications

(23 citation statements)

References 13 publications

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“…In this regard, a large number of empirical and semi-empirical relationships have been developed in the past decades, mainly from the corresponding equation of state to predict the crude oil viscosity. Most of the correlations presented have been established for a given region, so if used for other areas, erroneous results will be produced [34]. Empirical correlations are used to estimate dead oil, and saturated and undersaturated viscosities based on field data, but the output of these empirical correlations is typically unsatisfactory, and improvement is still sought [46].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Each study claimed that the resulting correlation would provide the best approximation of PVT properties for the local region compared to the other commonly used correlations. Studies performed by El-hoshoudy et al [4] ,Macary and Batanony [5],Hanafy et al [6], Glasso [7] ,Dokla and Osman [8] ,Marhoun [2], Labedi [9] all supported this conclusion [5]. Multiple linear/nonlinear least-squares regression analysis will be used to develop the new correlations after screening of the used parameters.…”

Section: Introductionmentioning

confidence: 82%

Mathematical Correlation Predicting Stock Tank Oil Density for Egyptian Crude Oils at Standard Conditions

El-hoshoudy¹

2014

OGCE

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Liquid density is one of the most important governing physical parameter of the fluid flow. Generally, oil density measured at standard conditions (14.65 psia and 60 °F) via Density Meter. Since there is a remarkable difference between calculated density from compositional analysis and measured one at standard conditions. So it is of great importance to use an accurate correlation to calculate the oil density at standard conditions. The proposed correlation correlates measured density with measured molecular weight, calculated molecular weight and calculated density which calculated from compositional analysis. Here, based on measured Egyptian oil reservoirs data; new correlation has been developed for prediction of oil density at standard conditions (14.65 psia and 60 °F). Validity and accuracy of this correlation has been confirmed by comparing the obtained results of this correlation with experimental data for Egyptian oil samples. Checking the results of this correlation shows that the developed correlation results are in a high agreement with the experimental data.

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“…The second one belongs to compositional models which rely on the equation of states and its hybrids. In these models besides PVT parameters, other measurements including fluid composition, critical temperature, molar masses and components acentric factor are utilized [12,13]. Although accuracy of the empirical PVT correlations is often limited due to variations and complexity of multicomponent reservoir fluid systems, these correlations still used on field scale [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

PVT Properties of Black Crude Oil

El-hoshoudy¹,

Desouky²

2019

Processing of Heavy Crude Oils - Challenges and Opportunities

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Precise PVT studies and behavior of phase-equilibrium of petroleum reservoir fluids are essential for describing these fluids and appraising their volumetric behavior at several pressure stages. There are numerous laboratory studies that can be performed on a reservoir sample. The amount of data desired determines the number of tests to be performed in the laboratory. Generally, there are three laboratory tests which characterize hydrocarbon fluids, namely primary study, constant mass depletion, and differential vaporization test. Generally, PVT properties are determined either experimentally or calculated theoretically through published correlations. This chapter presents different PVT laboratory tests that are required to understand the phase behavior of black oils.

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New correlations for prediction of viscosity and density of Egyptian oil reservoirs

Cited by 24 publications

References 13 publications

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

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

Mathematical Correlation Predicting Stock Tank Oil Density for Egyptian Crude Oils at Standard Conditions

PVT Properties of Black Crude Oil

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