Radial-basis-function-based nuclear magnetic resonance heavy oil viscosity prediction model for a Kuwait viscous oil field
Radial-basis-function-based nuclear magnetic resonance heavy oil viscosity prediction model for a Kuwait viscous oil field (in Advanced logs and interpretation, Yao Peng (prefacer), Vivek Anand (prefacer), Burkay Donderici (prefacer), Tie Sun (prefacer) and Xiaogang Han (prefacer))
Interpretation (Tulsa) (May 2016) 4 (2): SF81-SF92
Characterizing heavy oil viscosity by nuclear magnetic resonance (NMR) relaxation time (T (sub 1) and T (sub 2) ) measurements is much more challenging than characterizing light oil viscosities. Crude oils contain a wide range of hydrocarbons, resulting in broad T (sub 1) and T (sub 2) distributions that vary with the oil composition. Most often, a single geometric mean value T (sub 1, gm) or T (sub 2, 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 T (sub 1) and T (sub 2) distributions (T (sub 1, app) and T (sub 2, app) ) and apparent hydrogen index (HI (sub app) ). Using T (sub 1, app) and T (sub 2, 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 HI (sub app) at a fixed interecho time (TE), or a TE-dependent HI (sub app) . We have developed a new radial-basis-function-based heavy oil viscosity model using the entire T (sub 2,app) distribution, rather than T (sub 2, gm) , with an option of including the NMR-derived HI (sub app) . 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 T (sub 2, app) distributions, while discarding distribution features that are derived from random noise. The coefficients of the RBFs were derived using laboratory NMR T (sub 2) measurements at ambient and elevated temperatures between 23.5 degrees C and 39.5 degrees 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.