Iterative geostatistical history matching techniques based on stochastic sequential simulation, update the model parameters at each iteration globally or using a regional perturbation criterion (e.g., Voronoi polygons centred ad the well locations). While these techniques ensure the convergence of the iterative procedure towards the observed production data, the exploration of the model parameters, and the uncertainty space, is limited by each region considered. To avoid this effect, we propose the use of stochastic sequential simulation conditioned to local probability distributions at each grid cell to account for local uncertainties. The local probability distribution functions are built at the end of each iteration from the petrophysical realisations that generated simulated production curves with a misfit score below a given threshold. The proposed methodology was applied in a challenging non-stationary synthetic reservoir and the results show the advantages of the proposed technique when reproducing the geological features of interest when compared against conventional iterative geostatistical history matching.


Article metrics loading...

Loading full text...

Full text loading...


  1. Barrela, E., Azevedo, L., Demyanov, V.
    [2017] Geostatistical History Matching — A Zonation-based Approach Using Direct Sequential Simulation. 79th EAGE Conference and Exhibition 2017.
    [Google Scholar]
  2. Castro, S., Caers, J., Mukerji, T.
    [(2005] The Stanford VI reservoir. 18th Annual Report. Stanford Center for Reservoir Forecasting (SCRF). 1–73.
    [Google Scholar]
  3. Mata-Lima, H.
    [2008] Reservoir Characterization with Iterative Direct Sequential Co-simulation: Integrating Fluid Dynamic Data into Stochastic Model. Journal of Petroleum Science and Engineering. 62, 59–72.
    [Google Scholar]
  4. Oliveira, G. S., Soares, A., Schiozer, D., Maschio, C.
    [2017]. Reducing uncertainty in reservoir parameters combining history matching and conditioned geostatistical realizations. Journal of Petroleum Science and Engineering. 156, 75–90.
    [Google Scholar]
  5. Soares, A.
    [2001] Direct Sequential Simulation and Co-simulation. Mathematical Geology. 33, 911–926.
    [Google Scholar]
  6. Soares, A., Nunes, R. and Azevedo, L.
    [2017] Integration of Uncertainty Data in Geostatistical Modelling. Mathematical Geology. 49, 253–273.
    [Google Scholar]
  7. Tarantola, A.
    [1987] Inverse problem theory - methods for data fitting and model parameter estimation. Elsevier, ISBN: 0898715725.
    [Google Scholar]

Data & Media loading...

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