1887
  1. Home
  2. Conferences
  3. Conference Proceedings
  4. ECMOR XVII
  5. Conference paper

Abstract

Summary

Any complex phase behavior computation is a main challenge in reservoir simulation since it introduces high nonlinearities. To overcome this, an operator-based linearization (OBL) has been introduced recently. In OBL, an operator format is applied to represent the mass-based formulations. By computing the values of the operators related to rock and fluid properties on pre-defined status, the values of the operators and their derivatives on any status, which emerges during a simulation run, can be determined by interpolation. Obviously, the accuracy of the results is mainly controlled by the pre-defined status. In this work, we present a detailed investigation of the accuracy and efficiency of the OBL. To guarantee an objective evaluation, a novel advanced parallel framework is applied for reservoir simulation. In this framework, we implement a multipoint linearization method that is capable to provide accurate, robust, and convergent solutions for reservoir simulation. The number of points in the parametric space of each nonlinear known is defined as resolution. By running simulations at different resolutions, we compare the numerical solutions with analytical solutions. It shows that the resolution has a large effect on the accuracy of numerical solutions. We also investigate the robustness of the OBL by running simulations on several models with different complexity of the phase behavior. Besides, by looking into the convergent process, we also study the efficiency of the OBL method. Finally, we test several filed cases to show the performance of the OBL method for general-purpose reservoir simulations.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202035150
2020-09-14
2024-04-25

  • From This Site

    /content/papers/10.3997/2214-4609.202035150
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Aavatsmark, I., Barkve, T., Bøe, Ø., Mannseth, T.
    , 1994. Discretization on non-orthogonal, curvilinear grids for multi-phase flow. In Proceedings of the fourth European Conference on the Mathematics of Oil Recovery, Røros, Norway.
     [Google Scholar]
  2. , 1996a. Discretization on non-orthogonal, quadrilateral grids for inhomogeneous, anisotropic media.Journal of Computational Physics127: 2–14.
     [Google Scholar]
  3. , 1996b. A class of discretization methods for structured and unstructured grids in anisotropic, inhomogeneous media. In Proceedings of 5th European Conference on the Mathematics of Oil Recovery, Leoben, Austria.
     [Google Scholar]
  4. Aavatsmark, I., Barkve, T., Mannseth, T.
    , 1998. Control-volume discretization methods for 3D quadrilateral grids in inhomogeneous, anisotropic reservoirs.SPE J.3: 146–154.
     [Google Scholar]
  5. Abd, A.S., Abushaikha, A.
    , 2020. Velocity dependent up-winding scheme for node control volume finite element method for fluid flow in porous media.Sci Rep10, 442. https://doi.org/10.1038/s41598-020-61324-4
     [Google Scholar]
  6. Abushaikha, A. S., Blunt, M. J., Gosselin, O. R., et al.
    , 2015. Interface control volume finite element method for modelling multi-phase fluid flow in highly heterogeneous and fractured reservoirs.Journal of Computational Physics298: 41–61.
     [Google Scholar]
  7. Abushaikha, A. S., Voskov, D. V., Tchelepi, H. A.
    , 2017. Fully implicit mixed-hybrid finite-element discretization for general purpose subsurface reservoir simulation.Journal of Computational Physics346: 514–538.
     [Google Scholar]
  8. Abushaikha, A. S., Terekhov, K. M.
    , 2020. A fully implicit mimetic finite difference scheme for general purpose subsurface reservoir simulation with full tensor permeability.Journal of Computational Physics406: 109194.
     [Google Scholar]
  9. Alpak, F. O.
    , 2010. A mimetic finite volume discretization method for reservoir simulation.SPE J.15 (2): 436–453.
     [Google Scholar]
  10. Edwards, M. G., Rogers, C. F.
    , 1994. A flux continuous scheme for the full tensor pressure equation. In Proceedings of the fourth European Conference on the Mathematics of Oil Recovery, Røros.
     [Google Scholar]
  11. , 1998. Finite volume discretization with imposed flux continuity for the general tensor pressure equation.Computational Geosciences2 (4): 259–290.
     [Google Scholar]
  12. Hjeij, D., Abushaikha, A.
    , 2019a. An Investigation of the Performance of the Mimetic Finite Difference Scheme for Modelling Fluid Flow in Anisotropic Hydrocarbon Reservoirs. In SPE EAGE EUROPEC. Society of Petroleum Engineers.
     [Google Scholar]
  13. Hjeij, D., Abushaikha, A. S.
    , 2019b. Comparing Advanced Discretization Methods for Complex Hydrocarbon Reservoirs. In SPE Reservoir Characterisation and Simulation Conference and Exhibitions. Society of Petroleum Engineers.
     [Google Scholar]
  14. Khait, M., Voskov, D. V.
    , 2017. Operator-based linearization for general purpose reservoir simulation.Journal of Petroleum Science and Engineering157: 990–998.
     [Google Scholar]
  15. , 2018a. Operator-based linearization for efficient modeling of geothermal processes.Geothermics74: 7–18.
     [Google Scholar]
  16. , 2018b. Adaptive Parameterization for Solving of Thermal/ Compositional Nonlinear Flow and Transport with Buoyancy.SPE J.23 (02).
     [Google Scholar]
  17. Li, L., Yao, J., Li, Y., et al.
    , 2016. Pressure-transient analysis of CO2 flooding based on a compositional method.Journal of Natural Gas Science and Engineering33: 30–36.
     [Google Scholar]
  18. Li, L., Voskov, D. V., Yao, J., et al.
    , 2018a. Multiphase transient analysis for monitoring of CO2 flooding.Journal of Petroleum Science and Engineering160: 537–554.
     [Google Scholar]
  19. LiL., VoskovD. V.
    , 2018b. Multi-Level Discrete Fracture Model For Carbonate Reservoirs. In 16th European Conference on the Mathematics of Oil Recovery, 3–6 September, Barcelona, Spain.
     [Google Scholar]
  20. Li, L., Abushaikha, A. S.
    , 2019. Development of an Advancing Parallel Framework for Reservoir Simulation. In Third EAGE WIPIC Workshop: Reservoir Management in Carbonates, Doha, Qatar.
     [Google Scholar]
  21. Li, L., Khait, M., Voskov, D. V., Abushaikha, A. S.
    , 2020a. Parallel Framework for Complex Reservoir Simulation with AdvancedDiscretization and Linearization Schemes. In the SPE Europec featured at 82nd EAGE Conference and Exhibition, Amsterdam, Netherlands. SPE-200615-MS.
     [Google Scholar]
  22. Li, L., Abushaikha, A. S.
    , 2020b. An Advanced Parallel Framework for Reservoir Simulation with Mimetic Finite Difference Discretization and Operator-based Linearization. In 17th European Conference on the Mathematics of Oil Recovery, Edinburgh, United Kingdom.
     [Google Scholar]
  23. Lipnikov, K., Manzini, G., Shashkov, M.
    , 2014. Mimetic finite difference method.Journal of Computational Physics257: 1163–1227.
     [Google Scholar]
  24. Liu, L., Yao, J., Sun, H., et al.
    , 2018. Compositional modeling of shale condensate gas flow with multiple transportmechanisms.Journal of Petroleum Science and Engineering172: 1186–1201.
     [Google Scholar]
  25. Tikhonov, A. N., Samarskii, A. A.
    , 1962. Homogeneous difference schemes.Comput. Math. Math. Phys.1 (1): 5–67.
     [Google Scholar]
  26. Voskov, D. V.
    , 2017. Operator-based linearization approach for modeling of multiphase multi-component flow in porous media.Journal of Computational Physics337: 275–288.
     [Google Scholar]
  27. Wu, M., Ding, M., Yao, J., et al.
    , 2018. Pressure transient analysis of multiple fractured horizontal well in composite shale gas reservoirs by boundary element method.Journal of Petroleum Science and Engineering162: 84–101.
     [Google Scholar]
  28. Wang, Y., Voskov, D. V., Khait, M., Bruhn, D.
    , 2020. An efficient numerical simulator for geothermal simulation: A benchmark study.Applied Energy264: 114693.
     [Google Scholar]
  29. Zhang, N., Abushaikha, A. S.
    , 2019a. An Efficient Mimetic Finite Difference Method For Multiphase Flow In Fractured Reservoirs. In Society of Petroleum Engineers - SPE Europec Featured at 81st EAGE Conference and Exhibition 2019. SPE-195512.
     [Google Scholar]
  30. , 2019b. Fully Implicit Reservoir Simulation Using Mimetic Finite Difference Method inFractured Carbonate Reservoirs. In SPE Reservoir Characterisation and Simulation Conference and Exhibition, 17–19 September, Abu Dhabi, UAE. SPE-196711-MS.
     [Google Scholar]
  31. Zhu, G., Yao, J. Li, A., et al.
    , 2017. Pore-scale investigation of carbon dioxide-enhanced oil recovery.Energy and Fuels31(5): 5324–5332.
     [Google Scholar]
  32. Zhu, G., Kou, J., Yao, B., et al.
    , 2019. Thermodynamically consistent modelling of two-phase flows with moving contact line and soluble surfactants.Journal of Fluid Mechanics879: 327–359.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202035150
Loading
Investigation of the Accuracy and Efficiency of the Operator-based Linearization through an Advanced Reservoir Simulation Framework
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202035150
/content/papers/10.3997/2214-4609.202035150
/content/papers/10.3997/2214-4609.202035150
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error