This paper introduces the application to a real field case of an automatic iterative geostatistical history matching technique, integrating geological and engineering consistency. Current trends in the industry reflect a growing interest towards the development of workflows that simultaneously integrate petrophysical modeling with dynamic calibration of reservoir models to historical production data. Contrary to manual history matching techniques, where model perturbation often disregards geological or physical realism, the proposed history matching approach introduces geological consistency through geostatistical simulation and physical realism by using streamline regionalization while holding the predictive capability of resulting petrophysical models. In the proposed methodology, the reservoir static properties are iteratively updated by stochastic sequential simulation and co-simulation, constrained to production data, while streamline information is used for electing preponderant flow production regions of the model, as the focus for property perturbation. In order to capture the complex subsurface heterogeneities of the reservoir, petrophysical property realizations were obtained using the Direct Sequential Simulation and co-Simulation algorithm, with Multi-local Distribution Functions ( ). The location and proportion of reservoir facies is also automatically updated throughout the iterative procedure, using Bayesian Classification. The proposed approach was successfully applied to a real case study, located in North-East onshore Brazil, resulting in multiple history matched models that better reproduce historic data.


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  1. Kazemi, A., & Stephen, K. D.
    (2013). Optimal Parameter Updating in Assisted History Matching Using Streamlines as a Guide. Oil & Gas Science and Technology – Revue d’IFP Energies Nouvelles, 68(3), 577–594. https://doi.org/10.2516/ogst/2012071
    [Google Scholar]
  2. Le Ravalec-Dupin, M., & Da Veiga, S.
    (2011). Cosimulation as a perturbation method for calibrating porosity and permeability fields to dynamic data. Computers & Geosciences, 37(9), 1400–1412. https://doi.org/10.1016/j.cageo.2010.10.013
    [Google Scholar]
  3. Mata-Lima, H.
    (2008). Reducing uncertainty in reservoir modelling through efficient history matching. Oil Gas European Magazine, 3, 2008.
    [Google Scholar]
  4. Nunes, R., Soares, A., Azevedo, L., & Pereira, P.
    (2017). Geostatistical Seismic Inversion with Direct Sequential Simulation and Co-simulation with Multi-local Distribution Functions. Mathematical Geosciences, 49(5), 583–601. https://doi.org/10.1007/s11004-016-9651-0
    [Google Scholar]
  5. Soares, A.
    (2001). Direct sequential simulation and cosimulation. Mathematical Geology, 33(8), 911–926.
    [Google Scholar]

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