Improved Oil Viscosity Characterization by Low-Field NMR Using Feature Engineering and Supervised Learning Algorithms

Conventional methods for determining
and monitoring the viscosity
of oils are time-consuming, expensive, and in some instances, technically
unfeasible. These limitations can be avoided using low-field nuclear
magnetic resonance (LF-NMR) relaxometry. However, due to the chemical
dissimilarity of oils and various temperatures these oils are exposed
to, as well as LF-NMR equipment limitations, the commonly used models
fail to perform at a satisfactory level, making them impractical for
use in heavy oil and bitumen reservoirs and in environments with large
temperature oscillations (e.g., mechanical systems). We present a
framework that combines supervised learning algorithms with domain
knowledge for synthesizing new features to improve model forecasts
using only one NMR parameterT2 geometric mean. Two principal methods were considered, support vector
regression (SVR) and gradient boosted trees (GBRT). Models were trained
using the experimental data from our previous studies and literature
data combining conventional oils, heavy oils, and bitumens from various
reservoirs in Canada and United States. The models’ performance
was compared against four other intelligent algorithms and four well-known
empirical NMR models against which the SVR- and GBRT-based models
achieved the highest statistical scores. These two models can be used
for oil viscosity prediction in conventional and heavy oil reservoirs
with a wide range of oil viscosities and in situations where high
precision is needed, such as in the determination of viscosity of
petroleum distillates or for monitoring of oil viscosity in mechanical
systems. The proposed framework can also be applied to determine other
physicochemical properties of oils by LF-NMR, where the application
of supervised learning is usually impractical due to the limited volume
of experimental data.