Multiple Mixing-Cell Method for MMP Calculations

2008 ◽  
Author(s):  
Kaveh Ahmadi ◽  
Russell Taylor Johns
Keyword(s):  
Cell Method ◽  
Multiple Mixing

Multiple Mixing Cell Method for Three-Hydrocarbon-Phase Displacements

2014 ◽  
Author(s):  
Liwei Li ◽  
Saeid Khorsandi ◽  
Russell T. Johns ◽  
Kaveh Ahmadi
Keyword(s):  
Cell Method ◽  
Multiple Mixing

Modified Multiple Mixing Cell Method for Determining Minimum Miscibility Pressure

2016 ◽  
Vol 52 (5) ◽  
pp. 574-582
Author(s):  
Chenshuo Zhang ◽  
Zifei Fan ◽  
Anzhu Xu ◽  
Lisha Zhao
Keyword(s):  
Cell Method ◽  
Minimum Miscibility Pressure ◽  
Multiple Mixing

Multiple-Mixing-Cell Method for Three-Hydrocarbon-Phase Displacements

SPE Journal ◽  
2015 ◽  
Vol 20 (06) ◽  
pp. 1339-1349 ◽  
Author(s):  
Liwei Li ◽  
Saeid Khorsandi ◽  
Russell T. Johns ◽  
Kaveh Ahmadi
Keyword(s):  
Relative Permeability ◽  
Cell Model ◽  
Phase Identification ◽  
Displacement Efficiency ◽  
Compositional Simulation ◽  
Fractional Flow ◽  
Two Phase ◽  
Cell Method ◽  
Multiple Mixing ◽  
Three Phase

Summary Low-temperature oil displacements by carbon dioxide involve complex phase behavior, in which three hydrocarbon phases can coexist. Reliable design of miscible gasflooding requires knowledge of the minimum miscibility pressure (MMP), which is the pressure required for 100% recovery in the absence of dispersion or as defined by slimtube experiments as the “knee” in the recovery curve with pressure in which displacement efficiency is greater than 90%. There are currently no analytical methods to estimate the MMP for multicomponent mixtures exhibiting three hydrocarbon phases. Also, the use of compositional simulators to estimate MMP is not always reliable. These challenges include robustness issues of three-phase equilibrium calculations, inaccurate three-phase relative permeability models, and phase identification and labeling problems that can cause significant discontinuities and failures in the simulation results. How miscibility is developed, or not developed, for a three-phase displacement is not well-known. We developed a new three-phase multiple-mixing-cell method that gives a relatively easy and robust way to determine the pressure for miscibility or, more importantly, the pressure for high-displacement efficiency. The procedure that moves fluid from cell to cell is robust because it is independent of phase labeling (i.e., vapor or liquid), has a robust way to provide good initial guesses for three-phase flash calculations, and is also not dependent on three-phase relative permeability (fractional flow). These three aspects give the mixing-cell approach significant advantages over the use of compositional simulation to estimate MMP or to understand miscibility development. One can integrate the approach with previously developed two-phase multiple-mixing-cell models because it uses the tie-line lengths from the boundaries of tie triangles to recognize when the MMP or pressure for high-displacement efficiency is obtained. Application of the mixing-cell algorithm shows that, unlike most two-phase displacements, the dispersion-free MMP may not exist for three-phase displacements, but rather a pressure is reached in which the dispersion-free displacement efficiency is maximized. The authors believe that this is the first paper to examine a multiple-mixing-cell model in which two- and three-hydrocarbon phases occur and to calculate the MMP and/or pressure required for high displacement efficiency for such systems.

Multiple-Mixing-Cell Method for MMP Calculations

SPE Journal ◽  
2011 ◽  
Vol 16 (04) ◽  
pp. 733-742 ◽  
Author(s):  
Kaveh Ahmadi ◽  
Russell T. Johns
Keyword(s):  
Analytical Methods ◽  
Oil And Gas ◽  
Equilibrium Phase ◽  
Variable Number ◽  
Simulation Methods ◽  
Displacement Efficiency ◽  
Fractional Flow ◽  
Cell Method ◽  
Multiple Mixing ◽  
Tie Lines

Summary The minimum miscibility pressure (MMP) is a key parameter governing the displacement efficiency of gasfloods. There are several methods to determine the MMP, but the most accurate methods are slim-tube experiments, analytical methods, and numerical-simulation/cell-to-cell methods. Slim-tube experiments are important to perform because they use actual crude oil, but they are costly and time consuming. Analytical methods that use the method of characteristics (MOC) are very fast and help to understand the structure of gasfloods. MOC, however, relies on finding the unique and correct set of key tie lines in the displacements, which can be difficult. Slim-tube simulation methods and their simplified cell-to-cell derivatives require tedious fluid and rock inputs, and their MMP estimates can be clouded by dispersion. This paper presents a simple and accurate multiple-mixing-cell method for MMP calculations that corrects for dispersion, and is faster and less cumbersome than 1D simulation methods. Unlike previous mixing-cell methods, our cell-to-cell mixing model uses a variable number of cells, and is independent of gas/oil ratio, volume of the cells, excess oil volumes, and the amount of gas injected. The new method only relies on robust P/T flash calculations using any cubic equation-of-state (EOS). The calculations begin with only two cells and perform additional cell-to-cell contacts between resulting equilibrium-phase compositions based on equilibrium gas moving ahead of the equilibrium liquid phase. We show for a variety of oil and gas compositions that all key tie lines can be found to the desired accuracy, and that they are nearly identical to those found using analytical MOC methods. Our approach, however, is more accurate and robust than those from MOC because we do not make approximations regarding shocks along nontie-line paths, and the unique set of key tie lines converges automatically. The MMP using our mixing-cell method can be calculated in minutes using an Excel spreadsheet and is estimated from a novel bisection method of the minimum tie-line lengths observed in the cells at four or five pressures. Our multiple-mixing-cell method can calculate either the MMP or the minimum miscibility for enrichment (MME) independent of the number of components in the gas or oil. Our approach further supports the notion that the MMP is independent of fractional flow because we obtain the same key tie lines independent of how much fluid is moved from one cell to another.

Minimum Miscibility Pressure Determination: Modified Multiple Mixing Cell Method

2012 ◽  
Author(s):  
Tadesse Weldu Teklu ◽  
Shawket G. Ghedan ◽  
Ramona M. Graves ◽  
Xiaolong Yin
Keyword(s):  
Cell Method ◽  
Minimum Miscibility Pressure ◽  
Multiple Mixing

Cartesian cut-cell method for axisymmetric separating body flows

AIAA Journal ◽  
1999 ◽  
Vol 37 ◽  
pp. 905-911
Author(s):  
G. Yang ◽  
D. M. Causon ◽  
D. M. Ingram
Keyword(s):  
Cell Method ◽  
Cut Cell ◽  
Cartesian Cut

scholarly journals Finite cell method for functionally graded materials based on V-models and homogenized microstructures

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Benjamin Wassermann ◽  
Nina Korshunova ◽  
Stefan Kollmannsberger ◽  
Ernst Rank ◽  
Gershon Elber
Keyword(s):  
Functionally Graded Materials ◽  
Large Scale ◽  
Functionally Graded ◽  
Material Behavior ◽  
Modeling Framework ◽  
Finite Cell Method ◽  
Geometric Framework ◽  
Graded Materials ◽  
Cell Method ◽  
Finite Cell

AbstractThis paper proposes an extension of the finite cell method (FCM) to V-rep models, a novel geometric framework for volumetric representations. This combination of an embedded domain approach (FCM) and a new modeling framework (V-rep) forms the basis for an efficient and accurate simulation of mechanical artifacts, which are not only characterized by complex shapes but also by their non-standard interior structure. These types of objects gain more and more interest in the context of the new design opportunities opened by additive manufacturing, in particular when graded or micro-structured material is applied. Two different types of functionally graded materials (FGM) are considered: The first one, multi-material FGM is described using the inherent property of V-rep models to assign different properties throughout the interior of a domain. The second, single-material FGM—which is heterogeneously micro-structured—characterizes the effective material behavior of representative volume elements by homogenization and performs large-scale simulations using the embedded domain approach.

The Effectiveness of the Unit Cell Method in Numerically Modeling and Designing Liquid Cooled Heatsinks

2019 ◽  
Vol 11 (6) ◽  
Author(s):  
Ali C. Kheirabadi ◽  
Dominic Groulx
Keyword(s):  
Heat Transfer ◽  
Wall Temperature ◽  
Parametric Design ◽  
Unit Cell ◽  
Liquid Cooling ◽  
Cell Method ◽  
Numerical Predictions ◽  
Finite Element Package ◽  
Parametric Studies ◽  
Relative Differences

This study compares two numerical strategies for modeling flow and heat transfer through mini- and microchannel heatsinks, the unit cell approximation, and the full 3D model, with the objective of validating the former approach. Conjugate heat transfer and laminar flow through a 2 × 2 cm2 copper–water heatsink are modeled using the finite element package COMSOL Multiphysics 5.0. Parametric studies showed that as the heatsink channels’ widths were reduced, and the total number of channels increased, temperature and pressure predictions from both models converged to similar values. Relative differences as low as 5.4% and 1.6% were attained at a channel width of 0.25 mm for maximum wall temperature and channel pressure drop, respectively. Due to its computational efficiency and tendency to conservatively overpredict temperatures relative to the full 3D method, the unit cell approximation is recommended for parametric design of heatsinks with channels’ widths smaller than 0.5 mm, although this condition only holds for the given heatsink design. The unit cell method is then used to design an optimal heatsink for server liquid cooling applications. The heatsink has been fabricated and tested experimentally, and its thermal performance is compared with numerical predictions. The unit cell method underestimated the maximum wall temperature relative to experimental results by 3.0–14.5% as the flowrate rose from 0.3 to 1.5 gal/min (1.1–5.7 l/min).

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