The goal of history matching is to construct one or more reservoir models that match observed field behaviour and use those models to forecast field behaviour under different operating conditions. In constructing the model to be matched, there are a number of choices to be made, such as the type of geological model to construct and the number of unknown parameters to use for history matching. We often choose a single geological model and vary a set of parameters to give the best fit to the data (e.g. production history). There are two areas of concern with this approach. The first is that there are usually a number of possible geological models which are all plausible to some degree, and we may be able to make better forecasts by using this information. The second is that increasing the number of unknown parameters may give a better fit to the data, but may not predict so well if the model is over-fitted. The goal of this paper is to examine the application of two techniques to handle these problems. The first technique uses the concept of minimum description length (MDL), which was developed from information theory, and quantifies the trade-off between model complexity and goodness of fit. The second technique is called Bayesian Model Averaging, and was developed to improve the reliability of weather forecasts using codes developed at different centres by constructing a suitable average of the models which takes into account not only the uncertainty forecast by each model but also the between model variance. Both techniques are illustrated with real reservoir examples. The MDL approach is shown on a simple reservoir model based on a Gulf of Mexico Field. The BMA approach is shown on a field with a moderate number of injectors and producers.


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