A new framework for geostatistics-based history matching using genetic algorithm with adaptive bounds

• Geostatistical history matching (GHM) is a complex optimization problem.
• A new procedure for geostatistical history matching was proposed.
• A genetic algorithm (GA) with adaptive bounds is presented.
• The procedure makes feasible the use of GA methods in GHM.
• A realistic application is shown and promising results are reported.

To maintain geological consistency, it is necessary to carry out the history matching process integrated to the geostatistical modeling. However, this integration leads to a complex optimization problem because the relationship between the input and output variables can be highly nonlinear. The purpose of this paper is to present a framework to integrate the history matching of production and seismic-derived dynamic data through a genetic algorithm with adaptive bounds. A new procedure is proposed to reduce the range of the parameters during the optimization process. The methodology was applied to a synthetic reservoir model with structural and petrophysical properties similar to a real reservoir and the results showed that it is possible to apply genetic algorithm in the integration of history matching and geostatistical modeling with feasible computational effort in terms of number of flow simulations.