Water saturation determination is among the most challenging tasks in petrophysical well-logging, which directly impacts the decision-making process in hydrocarbon exploration and production. Low-field nuclear magnetic resonance (LF-NMR) measurements can provide reliable evaluation. However, quantification of oil and water volumes is problematic when their NMR signals are not distinct. To overcome this, we developed two machine learning frameworks for predicting relative water content in oil-sand samples using LF-NMR spin–spin (T2) relaxation and bulk density data to derive a model based on Extreme Gradient Boosting. The first one facilitates feature engineering based on empirical knowledge from the T2 relaxation distribution analysis domain and mutual information feature extraction technique, while the second model considers whole samples’ NMR T2-relaxation distribution. The NMR T2 distributions were obtained for 82 Canadian oil-sands samples at ambient and reservoir temperatures (164 data points). The true water content was determined by Dean-Stark extraction. The statistical scores confirm the strong generalization ability of the feature engineering LF-NMR model in predicting relative water content by Dean-Stark—root-mean-square error of 0.67% and mean-absolute error of 0.53% (R2 = 0.90). Results indicate that this approach can be extended for the improved in-situ water saturation evaluation by LF-NMR and bulk density measurements.
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 parameterT
2 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.
Nuclear Magnetic Resonance is widely used today in laboratory and borehole studies of rock porosity and permeability properties. Applying to oil-containing fluid physicochemical properties investigation this paper is devoted to Nuclear Magnetic Resonance Relaxometry possibilities study as an express-method of rheological properties evaluation and saturates, aromatics, resins and asphaltenes (SARA) analysis of hydrocarbons samples. Nuclear Magnetic Resonance Relaxometry properties of hydrocarbons samples from a number of fields in Western Siberia were studied. A comparative analysis with the results of standard geochemical studies was made.
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