A General Purpose Model for Multiphase Compositional Flow Simulation

Author(s):  
Shuhong Wu ◽  
Jiangyan Dong ◽  
Baohua Wang ◽  
Tianyi Fan ◽  
Hua Li
Author(s):  
Denis Voskov ◽  
Hamdi A. Tchelepi

In this work, we generalize the Compositional Space Parameterization (CSP) approach, which was originally developed for compositional two-phase reservoir flow simulation. Tie-line based parameterization methods [1]–[3] were motivated by insights obtained from MOC (Method of Characteristics) theory. The MOC based analytical theory [4] has provided deep understanding of the interactions between thermodynamics and flow. In our adaptive framework, tie-lines are used to represent the solution route of multi-component multiphase displacements. The tie-line information is used as a preconditioner for EOS computations in general-purpose compositional flow simulation.


2009 ◽  
Author(s):  
Hector Manuel Klie ◽  
Jorge Monteagudo ◽  
Hussein Hoteit ◽  
Adolfo Antonio Rodriguez

2017 ◽  
Vol 53 (4) ◽  
pp. 2917-2939 ◽  
Author(s):  
Kenneth M. Walton ◽  
Andre J. A. Unger ◽  
Marios A. Ioannidis ◽  
Beth L. Parker

2020 ◽  
Vol 17 (10) ◽  
pp. 562-580
Author(s):  
Martín E. Pérez Segura ◽  
Dean T. Mook ◽  
Sergio Preidikman

SPE Journal ◽  
2016 ◽  
Vol 21 (03) ◽  
pp. 0873-0887 ◽  
Author(s):  
Hangyu Li ◽  
Louis J. Durlofsky

Summary Compositional flow simulation, which is required for modeling enhanced-oil-recovery (EOR) operations, can be very expensive computationally, particularly when the geological model is highly resolved. It is therefore difficult to apply computational procedures that require large numbers of flow simulations, such as optimization, for EOR processes. In this paper, we develop an accurate and robust upscaling procedure for oil/gas compositional flow simulation. The method requires a global fine-scale compositional simulation, from which we compute the required upscaled parameters and functions associated with each coarse-scale interface or wellblock. These include coarse-scale transmissibilities, upscaled relative permeability functions, and so-called α-factors, which act to capture component flow rates in the oil and gas phases. Specialized near-well treatments for both injection and production wells are introduced. An iterative procedure for optimizing the α-factors is incorporated to further improve coarse-model accuracy. The upscaling methodology is applied to two example cases, a 2D model with eight components and a 3D model with four components, with flow in both cases driven by wells arranged in a five-spot pattern. Numerical results demonstrate that the global compositional upscaling procedure consistently provides very accurate coarse results for both phase and component production rates, at both the field and well level. The robustness of the compositionally upscaled models is assessed by simulating cases with time-varying well bottomhole pressures that are significantly different from those used when the coarse model was constructed. The coarse models are shown to provide accurate predictions in these tests, indicating that the upscaled model is robust with respect to well settings. This suggests that one can use upscaled models generated from our procedure to mitigate computational demands in important applications such as well-control optimization.


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