Abstract
This paper presents a case study using a Multipurpose Environment for Parallel Optimization with application to assisted History Matching. Evolutionary Algorithms and deterministic optimization schemes are integrated into a workflow controlling a large number of parallel reservoir simulations.
The practical applicability of a software assisted history matching process is illustrated on basis of a real case study. The history matching process is discussed for an inverted 5-spot water flood pattern that had been subject to an extensive program of reservoir pressure and water saturation monitoring. Results are analyzed and compared to traditional history matching to identify the potential added valued and increased efficiency.
The quality of the manual history match was reproduced for pressure data and significantly improved for saturation data. The resulting model has proven to show better forward modeling characteristics defined by matching of some blind test data. Due to highly constraining boundary conditions defined by pressure and saturation data, it was not possible to generate multiple solutions to this specific problem. Within the given constraints, the model tuning parameters had to be close to the values of the reference case to be able to match such constraining pressure and saturation data.
In conclusion, the software assisted history matching process was able to improve the quality of the match within less time and effort. A lesson learned process is discussed focusing on the engineer acquiring more information and improving the understanding on reservoir uncertainties and the reservoir model behavior.
