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SPE-154477-PA

A Comparative Study of Reduced-Variables-Based Flash and Conventional Flash

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DOI  More information 10.2118/154477-PA http://dx.doi.org/10.2118/154477-PA

Citation

  • Michelsen, M.L., Yan, W., and Stenby, E.H. 2013. A Comparative Study of Reduced-Variables-Based Flash and Conventional Flash. SPE J. SPE-154477-PA (in progress; posted 31 January 3013).

Summary

For compositional transient simulations including compositional reservoir simulations, phase-equilibrium calculation, often formulated as a flash problem, can be time consuming. It is therefore important to speed up the calculation of phase equilibrium to improve the efficiency of the simulator. The reduced-variables methods, or the reduction methods, reformulate the original phase equilibrium problem with a smaller set of independent variables. Various versions of the reduced-variables methods have been proposed since the mid-1980s. The methods were first proposed for cubic equations of state (EOSs) with zero binary interaction parameters (BIPs) and later generalized to situations with nonzero-BIP matrices. Most of the studies in the last decade suggest that the reduced-variables methods are much more efficient than the conventional flash method. However, Haugen and Beckner (2011) questioned the advantages of the reduced-variables methods in their recent paper. A fair comparison between the reduced-variables-based flash and the conventional flash is not straightforward because it is difficult to formulate the former as unconstrained minimization problems, and the flash calculation time is also related to the implementation quality. With the recent formulations by Nichita and Graciaa (2011), it is possible to code the reduced-variables methods without extensive modifications of Michelsen's conventional flash algorithm. A minimization-based reduced-variables algorithm was coded and compared with the conventional minimization-based flash. A test with the use of the SPE 3 example (Kenyon and Behie 1987) showed that the best reduction in time was less than 20% for the extreme situation of 25 components and just one row/column with nonzero BIPs. A better performance can be achieved by a simpler implementation directly using the sparsity of the BIP matrix.

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History

  • Original manuscript received: 13 March 2012
  • Meeting paper published: 4 June 2012
  • Revised manuscript received: 25 October 2012
  • Manuscript approved: 31 October 2012
  • Published online: 31 January 2013