Abstract

Characterizing heavy oil viscosity by nuclear magnetic resonance (NMR) relaxation time (T1 and T2) measurements is much more challenging than characterizing light oil viscosities. Crude oils contain a wide range of hydrocarbons, resulting in broad T1 and T2 distributions that vary with the oil composition. Most often, a single geometric mean value T1,gm or T2,gm is correlated with the crude oil viscosity, which cannot accurately account for the inherent complexity of the oil constituent information. Furthermore, as the viscosity increases, some of the protons in the oil relax too quickly to be observable by logging or laboratory NMR instruments. This results in deficiencies of relaxation time and signal amplitude that give rise to apparent T1 and T2 distributions (T1,app and T2,app) and apparent hydrogen index (HIapp). Using T1,app and T2,app distributions in NMR viscosity models could produce erroneous heavy oil viscosity estimations. Several attempts have been made to overcome these challenges by taking into account HIapp at a fixed interecho time (TE), or a TE-dependent HIapp. We have developed a new radial-basis-function-based heavy oil viscosity model using the entire T2,app distribution, rather than T2,gm, with an option of including the NMR-derived HIapp. Because both of these quantities are TE dependent, it is desirable to include multiple TE data to develop the model. In addition, the principal component analysis (PCA) method was applied to extract major variations of features embedded in the T2,app distributions, while discarding distribution features that are derived from random noise. The coefficients of the RBFs were derived using laboratory NMR T2 measurements at ambient and elevated temperatures between 23.5°C and 39.5°C and corresponding viscosity measurements on 50 oil samples. These oil samples were collected from different parts of a shallow viscous oil reservoir in Kuwait. It was observed that the use of this newly developed RBF method showed significant improvement in terms of the reliability of the viscosity prediction compared to some recently published heavy oil viscosity correlations.

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