Matrix decomposition methods for accurate water saturation prediction in Canadian oil-sands by LF-NMR T2 measurements

This article presents a novel method for quantifying water saturation in oil-sand reservoirs by employing 1D lowfield nuclear magnetic resonance (LF-NMR) spin-spin relaxation and bulk density measurements as indicators of pore volume variations. One of the challenges in accurately determining the volumes of bitumen and water in oilsands is the effective separation of their overlapping T2 signals, attributed to similar spin-spin relaxation decay times and diffusive coupling. Conventional methods require deconvolution of T2 peaks and or experimentation to determine T2 cutoff values, differentiating between bitumen and water signals, notably capillary and clay-bound water. In contrast, our approach predicts the proportion of water by utilizing matrix decomposition methods to compress the T2 relaxation distribution and extract significant components. These components subsequently train the regression model, facilitating the accurate estimation of relative water saturation percentages.The NMR dataset was obtained by benchtop LF-NMR T2 measurements from 82 oil-sand samples, with preserved bitumen and water saturations at both reservoir and ambient temperatures (6 degrees C and 25 degrees C), yielding 164 observations. We examined four matrix decomposition methods, including principal component analysis, its variation integrating a kernel function, canonical correlation analysis, and partial least squares regression. X-ray CT measurements and Dean-Stark extraction ascertained the respective sample bulk densities and fluid-solid volume proportions.The PCA model prediction statistics (RMSE = 0.86%, R2 = 0.84), indicate its application can be extended for saturation prediction from NMR and bulk density well logs. Moreover, we underscore the importance of incorporating bulk density measurements and establish the statistical and physical correlations between these measurements and NMR T2 relaxation, providing insights into the approach's efficacy and causality.