Computational Geosciences

, Volume 22, Issue 1, pp 3–28 | Cite as

Identifying influence areas with connectivity analysis – application to the local perturbation of heterogeneity distribution for history matching

  • Véronique Gervais
  • Mickaële Le Ravalec
Original Paper


Numerical representations of a target reservoir can help to assess the potential of different development plans. To be as predictive as possible, these representations or models must reproduce the data (static, dynamic) collected on the field. However, constraining reservoir models to dynamic data – the history-matching process – can be very time consuming. Many uncertain parameters need to be taken into account, such as the spatial distribution of petrophysical properties. This distribution is mostly unknown and usually represented by millions of values populating the reservoir grid. Dedicated parameterization techniques make it possible to investigate many spatial distributions from a small number of parameters. The efficiency of the matching process can be improved from the perturbation of specific regions of the reservoir. Distinct approaches can be considered to define such regions. For instance, one can refer to streamlines. The leading idea is to identify areas that influence the production behavior where the data are poorly reproduced. Here, we propose alternative methods based on connectivity analysis to easily provide approximate influence areas for any fluid-flow simulation. The reservoir is viewed as a set of nodes connected by weighted links that characterize the distance between two nodes. The path between nodes (or grid blocks) with the lowest cumulative weight yields an approximate flow path used to define influence areas. The potential of the approach is demonstrated on the basis of 2D synthetic cases for the joint integration of production and 4D saturation data, considering several formulations for the weights attributed to the links.


History matching Reservoir partitioning Influence area Connectivity analysis Local perturbation Parameterization Cross-covariance 


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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.IFP Energies nouvellesRueil-MalmaisonFrance

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