Radial-basis-function-based nuclear magnetic resonance heavy oil viscosity prediction model for a Kuwait viscous oil field

Abstract

Characterizing heavy oil viscosity by nuclear magnetic resonance (NMR) relaxation time ([Formula: see text] and [Formula: see text]) measurements is much more challenging than characterizing light oil viscosities. Crude oils contain a wide range of hydrocarbons, resulting in broad [Formula: see text] and [Formula: see text] distributions that vary with the oil composition. Most often, a single geometric mean value [Formula: see text] or [Formula: see text] 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 [Formula: see text] and [Formula: see text] distributions ([Formula: see text] and [Formula: see text]) and apparent hydrogen index ([Formula: see text]). Using [Formula: see text] and [Formula: see text] 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 [Formula: see text] at a fixed interecho time (TE), or a TE-dependent [Formula: see text]. We have developed a new radial-basis-function-based heavy oil viscosity model using the entire [Formula: see text] distribution, rather than [Formula: see text], with an option of including the NMR-derived [Formula: see text]. 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 [Formula: see text] distributions, while discarding distribution features that are derived from random noise. The coefficients of the RBFs were derived using laboratory NMR [Formula: see text] 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.