Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
2011-06-14
Physics
Condensed Matter
Disordered Systems and Neural Networks
Scientific paper
We investigate the effect of a temperature gradient on oil recovery in a two-dimensional pore-network model. The oil viscosity depends on temperature as, $\mu_o=exp(B/T)$, where $B$ is a physico-chemical parameter depending on the type of oil, and $T$ is the temperature. A temperature gradient is applied across the medium in the flow direction. Initially, the porous medium is saturated with oil and, then, another fluid is injected. We have considered two cases representing different injection strategies. In the first case, the invading fluid viscosity is constant (finite viscosity ratio) while in the second one, the invading fluid is inviscid (infinite viscosity ratio). Our results show that, for the case of finite viscosity ratio, recovery increases with $\Delta T$ independently on strength or sign of the gradient. For an infinite viscosity ratio, a positive temperature gradient is necessary to enhance recovery. Moreover, we show that, for $\Delta T>0$, the percentage of oil recovery generally decreases (increases) with $B$ for a finite (infinite) viscosity ratio. Finally, we also extend our results for infinite viscosity ratio to a three-dimensional porous media geometry.
Andrade Jose S. Jr.
Herrmann Hans Jürgen
Oliveira Claudio L. N.
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