1887

Abstract

Summary

This paper shows the application to a real field case of an iterative geostatistical history matching technique, integrating geological and engineering consistency. Current trends reflect a growing interest on developing 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 leading to poor production forecast, this example introduces geological consistency through geostatistical simulation and physical realism by using streamline regionalization, while holding the predictive capability of resulting petrophysical models. This is achieved by iteratively updating the reservoir static properties using stochastic sequential simulation and co-simulation, constrained to production data, while using streamline information for electing preponderant flow production regions of the model, focusing property perturbation. In order to capture the complex subsurface heterogeneities of the reservoir, petrophysical property realizations are obtained using the direct sequential simulation and co-simulation with multi-local distribution functions. The location and proportion of reservoir facies is also automatically updated throughout the iterative procedure, using Bayesian Classification. The technique 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.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201902204
2019-09-02
2024-04-25
Loading full text...

Full text loading...

References

  1. Barrela, E., Azevedo, L., & Demyanov, V. [2017]. Geostatistical History Matching-A Zonation-based Approach Using Direct Sequential Simulation. In 79th EAGE Conference and Exhibition 2017. https://doi.org/10.3997/2214-4609.201700970
    [Google Scholar]
  2. Barrela, E., Azevedo, L., Soares, A., & Guerreiro, L. [2018]. Integrating consistent streamline regionalization and multi-local distribution functions into geostatistical history matching: A real case application. In RDPETRO 2018: Research and Development Petroleum Conference and Exhibition (pp. 136–139). Abu Dhabi, UAE. https://doi.org/10.1190/RDP2018-35367099.1
    [Google Scholar]
  3. 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]
  4. 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]
  5. Mata-Lima, H. [2008]. Reducing uncertainty in reservoir modelling through efficient history matching. Oil Gas European Magazine, 3, 2008.
    [Google Scholar]
  6. 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]
  7. Soares, A. [2001]. Direct sequential simulation and cosimulation. Mathematical Geology, 33(8), 911–926.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201902204
Loading
/content/papers/10.3997/2214-4609.201902204
Loading

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