History matching in reservoir modeling is done increasingly with the help of (semi-) automated computer techniques. Although such techniques will lead to accelerated estimation of the values of reservoir model parameters, they will not, by themselves, solve the often occurring problem of conceptual reservoir model inadequacies (missing faults, unidentified aquifers, etc.). Closing the loop between the various disciplines cannot - should not! - be automated. Over the past years, we have developed a workflow called “Model Maturation”, where we use a forced history match to flag inadequacies in the dynamic model, and subsequently address those in an interdisciplinary dialogue (between reservoir engineer, geologist, petrophysicist, geophysicist). Such a forced history match is not limited to production data, but can also include data from 4D seismic, saturation logs and RFT measurements. The “matured” reservoir model is subsequently subjected to a standard Assisted History Match, using, for example, DoE to optimize the values of the revised set of reservoir parameters. In this presentation, we show the concepts and a few (of many) field cases worldwide where we have successfully applied abovementioned workflow, thereby significantly improving the reservoir models in question, which in turn has a direct impact on the business decisions depending on those models.


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