Reservoir characterization and forecasting require construction of reliable and history matched reservoir models. In gradient-based automatic history matching procedure, most of the CPU time is spent on the sensitivity calculation. When the adjoint algorithm is used to calculate the sensitivity, the CPU time is approximately proportional to the magnitude of observed data. If we can identify observed data with most information content, we can minimize the history matching time using adjoint method and hence make the history matching faster.

A methodology has been developed in this work to identify the important production data and discard those "less useful" data in the automatic history matching procedure. This enables us to save significant computational time without sacrificing much of the history matching result. Our approach ensures that the selected observed data contains little redundancy, and gives similar inverted result based on the contents of all the observed data set.

The method is validated by conducting history matches for synthetic data sets. The comparisons of computational time and the quality of the inverted results indicate that this method is promising and practical for the automatic history matching. Using this approach, computational effort can be reduced by as much as ten times without sacrificing the quality of the results.

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