For field development scenarios hydrodynamic models acquired particular relevance, as they most fully include the entire set of available geological and geophysical data from the reservoir. In a standard history matching process based on a single geological model, achieving a good fit may lead to extreme, non-geological values of petrophysical properties in the near-wellbore areas. Input data for modeling are often determined with significant uncertainty and cover only a small fraction of the reservoir; thus, the use of a single history-matched model fails to account for these inherent uncertainties. All of this can significantly reduce the predictive capabilities of such models and increase the risks in development decision-making, particularly for under-explored reservoirs or greenfields. Methods for assisted history matching of hydrodynamic models based on ensembles of geological models are considered in the article. This method enables to include existing uncertainties of the initial geological and petrophysical data, and obtain a set of hydrodynamic models matched to the actual production data during optimization. The advantages of this approach in automation of the process and reduction of possible errors (extreme values of properties). The methods were developed and tested on a real field, considered various optimization approaches and algorithms. Based on the results, the quality and compliance with the adaptation criteria assessed.
References
1. Bianco A., Cominelli A., Dovera L. et al., History matching and production forecast uncertainty by means of the ensemble kalman filter – A real field application,
SPE-107161-MS, 2007, DOI: https://doi.org/10.2118/107161-MS
2. Seiler A., Evensen G., Skjervheim J.-A. et al., Advanced reservoir management workflow using an EnKF based assisted history matching method, SPE-118906-MS, 2009, DOI: https://doi.org/10.2118/118906-MS
3. Peters E., Arts R.J., Brouwer G.K., Geel C.R., Results of the Brugge benchmark study for flooding optimization and history matching, SPE-119094-MS, 2009,
DOI: https://doi.org/10.2118/119094-MS
4. Evensen G., Data assimilation: The ensemble Kalman filter, Springer, 2007, 272 p., DOI: https://doi.org/10.1007/978-3-540-38301-7
5. Eremyan G.A., Vybor tselevoy funktsii dlya resheniya zadachi avtoadaptatsii geologo-gidrodinamicheskoy modeli (Selection of the objective function for solving the problem of automatic adaptation of a geological-hydrodynamic model): thesis of candidate of technical science, Tomsk, 2020.
6. Vremennyy reglament otsenki kachestv pat i priemki trekhmernykh tsifrovykh geologo-gidrodinamicheskikh modeley (Temporary regulations for assessing the quality of paths and accepting three-dimensional digital geological and hydrodynamic models), Moscow: Publ. of Rosnedra, 2012.
7. IRM. Rukovodstvo pol’zovatelya tNavigator. Adaptatsiya i Optimizatsiya (IRM. User’s Guide tNavigator. Adaptation and Optimization), 2024.
8. Cancelliere M., Verga F., Viberti D., Benefits and limitations of assisted history matching, SPE-146278-MS, 2011, DOI: https://doi.org/10.2118/146278-MS