History matching is the process of modifying the uncertain attributes of a reservoir model to reproduce the real reservoir performance. It is a classical reservoir engineering problem and plays an important role in reservoir management since the resulting models are used to support decisions in other tasks such as economic analysis and production strategy.
In this paper, the authors present a dynamic decision-making optimization framework for history matching. The term ‘dynamic decision-making’ reflects the fact that, during the framework execution, the decision to generate a particular new solution is always guided and supported by the results of a continuous and dynamic analysis of the data from available solutions.
The proposed framework is different from previous approaches reported in the literature in the following aspects: it is not a stochastic method, since there is no randomness in its execution, nor it requires a large number of simulations to converge; it does not use a proxy model to substitute the flow simulator, so the results obtained with the framework are accurate at any moment of the execution; it is not a geostatistical process neither is primarily concerned with uncertainty reduction of the reservoir attributes. Rather, it is an optimization framework which follows a learning approach where the strategy is to dynamically analyze a set of observations (available solutions) to uncover input patterns (values of reservoir uncertain attributes) that lead to desired responses (good history matching for one or more wells) in the available solutions.
Cavalcante CCB, Maschio C, Santos AA, Schiozer D, Rocha A (2017) History matching through dynamic decision-making. PLOS ONE 12(6): e0178507.https://doi.org/10.1371/journal.pone.0178507