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Accounting for diagenesis overprint in carbonate reservoirs using parametrization technique and optimization workflow for production data matching

  • León Carrera, Maria Fernanda1, 2
  • Barbier, Mickaël2
  • Le Ravalec, Mickaele2
  • 1 Repsol Technology and New Ventures, Upstream Direction, Calle Agustín de Betancourt s/n, 28935, Móstoles, Spain , 28935 (Spain)
  • 2 IFP Energies Nouvelles, 1 & 4, avenue de Bois-Préau, Rueil – Malmaison, 92852, France , Rueil – Malmaison (France)
Published Article
Journal of Petroleum Exploration and Production Technology
Springer International Publishing
Publication Date
Feb 20, 2018
DOI: 10.1007/s13202-018-0446-3
Springer Nature


Diagenesis is rarely accounted for in the standard modeling workflows for carbonate reservoirs, although it has a huge impact on both porosity and permeability. This can be explained by at least two reasons: first, it is difficult to quantify the influence of diagenetic overprints on porosity and permeability; second, the integration of the diagenetic effects in carbonate reservoir models makes history matching much more difficult. Herein, a modeling methodology is proposed, in which the diagenetic imprints are included in the reservoir model and calibrated with dynamic data. The key point consists in defining a parametrization technique able to capture these diagenetic imprints. We assume that distinct regions of occurrence of a given diagenetic phase can be identified within the reservoir. Therefore, restricting our attention to a facies, we may distinguish regions characterized by low, medium or high proportions of the targeted diagenetic phase. The advantage of this parametrization technique is that the proportions of these regions can be easily driven by a reduced number of proportionality coefficients. Then, the overall modeling approach is integrated in an optimization workflow making it possible to vary the proportions of the region with a given occurrence for a given diagenetic phase, the variograms characterizing the spatial distribution of the regions, or even the way they are spatially distributed. The optimization process is run to adjust these various unknown parameters in order to match production history. The potential of the proposed methodology is finally investigated through the study of a two-dimensional numerical example.

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