scholarly journals Reduced-Order Modeling of Subsurface Multi-phase Flow Models Using Deep Residual Recurrent Neural Networks

2018 ◽  
Vol 126 (3) ◽  
pp. 713-741 ◽  
Author(s):  
J. Nagoor Kani ◽  
Ahmed H. Elsheikh
2011 ◽  
Vol 230 (22) ◽  
pp. 8304-8312 ◽  
Author(s):  
C.-H. Park ◽  
N. Böttcher ◽  
W. Wang ◽  
O. Kolditz

Author(s):  
David A. DiCarlo

There has been great recent interest in dynamic models of multi-phase flow. This is for two reasons: one, theoretical arguments suggest that the traditional multi-phase flow equations are not complete; two, various experimental measurements are unable to be described by the traditional models. In this talk, we discuss the observation that constant flux infiltrations into sands produce non-monotonic saturation and pressure profiles. We show how this non-monotonic behavior is the strongest evidence of dynamic effects in porous media, as other reported experimental evidence can be the result of varying measuring volumes, and/or media heterogeneities. Thus the extensive data set obtained for these non-monotonic provides the best testing ground for the various proposed dynamic extensions.


Author(s):  
К.А. Новиков

Сформулированы и доказаны принципы максимума для нескольких моделей многофазной фильтрации. Первый принцип справедлив для фазовых насыщенностей в несжимаемом случае модели двухфазной фильтрации с постоянными вязкостями, а второй - для глобального давления в моделях двух- и трехфазной фильтрации Two maximum principles for several multi-phase flow models are formulated and proved. The first one is valid for phase saturations in an incompressible two-phase flow model with constant viscosities. The second one is valid for the global pressure in two- and three-phase flow models with constant viscosities and is also valid for phase pressures in the case of zero capillary pressure.


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