History matching is a very important activity during the development and management of petroleum reservoirs. Matched models are fundamental to ensure reliable future forecasts, and enhance the level of understanding of the field via geological and reservoir models. Automated history matching (Figure 1) uses a mathematical algorithm to help choose new parameter values so that models better match historical data. The algorithm may be deterministic such as a gradient based method or stochastic such as a genetic or neighbourhood algorithm (NA). The former finds optimal solutions rapidly but the overall search is limited. Locally optimum solutions are all that may be found and uncertainty analysis is not effective. Stochastic methods are quasi-global but can be quite expensive. In this work we consider an approach to speed up the convergence rate of the NA. A proxy model is used to direct the stochastic search using gradients more effectively than is usual for the NA (Arwini and Stephen 2010). We call the new method Neighbourhood Algorithm with Proxy derived Gradients (NAPG). This results in finding solutions with far fewer models. The approach is further improved by updating the proxy model as we progress with history matching and the initialization is optimized using experimental design methods. We apply the approach to the Schiehallion field where we also use time lapse (4D) seismic data as dynamic constraints to reduce uncertainty to get a more reliable model that we might use it afterwards for the forecasting.


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