Summary
This research delivers a comprehensive analysis of complex permittivity’s sensitivity—a key parameter in characterizing electromagnetic (EM) wave absorption and penetration dynamics within oil reservoirs—to various reservoir properties including temperature, pressure, mineralogy (limestone, quartz, and clays), and water saturation. Contemporary estimation techniques, primarily grounded in simplistic mixing rule models, often fail to capture the intricacy inherent to heavy oil reservoirs. Thus, this study, buttressed by extensive experimental data, introduces a distinctive, intrinsically multivariable regression model that better delineates the relationships between dielectric properties and reservoir conditions.
Our investigation uncovers water saturation as a pivotal factor in the sensitivity of the regression model, largely due to water’s inherently polar nature. We note that the complex permittivity of the sample diminishes with increasing quartz content and decreasing limestone content—despite limestone possessing a higher individual dielectric constant. Moreover, the presence of clays, primarily found within the pore space delineated by the quartz and limestone sand’s grain boundaries, accentuates the expansive sensitivity of the dielectric constant to water volume, overshadowing the polarizability induced by the double-layer effect of water-sensitive clays.
The study additionally uncovers temperature-dependent behavior, characterized by an increase in both components of complex permittivity with rising temperatures, particularly for composite materials comprising diverse rock minerals and hydrocarbons. We also explore pressure as a variable and find no discernible impact on complex permittivity due to pressure fluctuations, although samples with high quartz content exhibit dynamic polarization under pressure.
Our multivariable regression model, responsive to all identified parameters, offers a more accurate and realistic interpretation of complex permittivity within the reservoir. The model’s validation on assorted unconsolidated and consolidated core samples underpins its reliability, paving the way for its implementation in any model estimating absorption and penetration dynamics within the reservoir.
In summation, the results of this study hold significant implications for reservoir simulation. The regressed models can be directly integrated to bridge the gap between numerical simulation and experimentally obtained relationships. These findings offer insights into the behavior of complex permittivity and its sensitivity to various reservoir properties. The regression model developed herein provides a more nuanced approach to estimating complex permittivity in oil reservoirs, enhancing our comprehension of EM wave absorption dynamics, and contributing to the advancement of more efficient and sustainable oil extraction methodologies.
