Static connectivity measures have been proposed for quick evaluation of reservoir performance to provide a potentially important link between the reservoir characterization and the simulation studies. These measures are easy in concept and inexpensive in execution, and create an important, intermediate level in the assessment of reservoir productivity. This paper proposes a framework for static connectivity analysis in reservoirs that use water flooding technique and pressure propagation fronts as it used in well testing. The software uses a fast marching method and a shortest path algorithm that both are sensitive to geological heterogeneities which can give some insights into finding the connective geobodies. An illustrative example is shown to describe the software interface and to present a simple but systematic connectivity analysis scenario. The distinct tasks contained in a typical reservoir development workflow may be benefited from the addition of connectivity analysis, such as the assessment of optimum well placements for injection-production wells and the evaluation of features of stratigraphic architectures that affect the recovery. The proposed tool is towards providing a geoengineering approach to use the geological knowledge for proposing better production scenarios.


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  1. Deutsch, C.V.
    [2002] Geostatistical Reservoir Modelling. USA, Oxford University Press.
    [Google Scholar]
  2. Fenik, D.R. et al.
    [2009] Ranking Realizations for SAGD Performance Predictions. Published report http://www.ccgalberta.com/ccgresources/report11/2009-204_ranking_realizations_for_sagd.pdf.
    [Google Scholar]
  3. Hamdi, H.
    [2014] Well-test Response in Stochastic Permeable Media. Journal of Petroleum Science and Engineering, 119(0), 169–184.
    [Google Scholar]
  4. Hird, K.B., and Dubrule, O.
    [1998] Quantification of Reservoir Connectivity for Reservoir Description Applications. Society of Petroleum Engineers. doi: 10.2118/30571‑PA.
    https://doi.org/10.2118/30571-PA [Google Scholar]
  5. Hovadik, J.M., and Larue, D.K.
    [2010] Stratigraphic and Structural Connectivity. Geological Society, London, Special Publications, 347, 219–242.
    [Google Scholar]
  6. Li et al.
    [2012] Ranking Geostatistical Reservoir Models with Modified Connected Hydrocarbon Volume. International Geostatistics Congress 2012, Oslo, Norway.
    [Google Scholar]
  7. MohammedS.R., and AhmadJ.S.
    [2012] Water Injection Optimization Using Streamlines from a Finite-Difference Simulator: A Case Study of a Middle East Field. Society of Petroleum Engineers. doi: 10.2118/160895‑MS.
    https://doi.org/10.2118/160895-MS [Google Scholar]
  8. Moreno, G., and Lake, L.
    [2014] On the Uncertainty of Interwell Connectivity Estimations from the Capacitance-resistance Model. Petroleum Science, 11(2), 265–271.
    [Google Scholar]
  9. Sadeghnejad, S. et al.
    [2011] Utilization of percolation approach to evaluate reservoir connectivity and effective permeability: A case study on North Pars gas field. Scientific Iranica, 18(6), 1391–1396.
    [Google Scholar]
  10. Sethian, J. A.
    (1996). “A fast marching level set method for monotonically advancing fronts.”Proceedings of the National Academy of Sciences93(4): 1591–1595.
    [Google Scholar]
  11. Sharifi, M. et al.
    [2014] Dynamic Ranking of Multiple Realizations by Use of the Fast-Marching Method. Society of Petroleum Engineers. doi: 10.2118/169900‑PA.
    https://doi.org/10.2118/169900-PA [Google Scholar]
  12. Strebelle, S.
    [2000] Sequential Simulation Drawing Structures from Training Images. PhD thesis, Stanford University.
    [Google Scholar]

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