Although Principal Component Analysis (PCA) has been widely applied to effectively reduce the number of parameters characterizing a reservoir, its disadvantages are well recognized by researchers. First, PCA may distort the probability distribution function (PDF) of the original model, especially for non-Gaussian properties such as facies indicator or permeability field of a fluvial reservoir. Second, it smears the boundaries between different facies. Therefore, the models reconstructed by PCA are generally unacceptable for geologists.

A workflow is proposed to seamlessly integrate Cumulative-Distribution-Function-based PCA (CDF-PCA) and streamline information for assisted-HM on a two-facies channelized reservoir. The CDF-PCA is developed to reconstruct reservoir models using only a few hundred of principal components. It inherits the advantage of PCA to capture the main features or trends of spatial correlations among properties, and more importantly, it can properly correct the smoothing effect of PCA. Integer variables such as facies indicators are regenerated by truncating their corresponding PCA results with thresholds that honor the fraction of each facies at first, and then real variables such as permeability and porosity are regenerated by mapping their corresponding PCA results to new values according to the CDF curves of different properties in different facies. Therefore, the models reconstructed by CDF-PCA preserve both geological (facies fraction) and geostatistical (non-Gaussian distribution with multi-peaks) characteristics of their original or prior models. Our preliminary results indicate that the history-matched model using the CDF-PCA alone may not satisfy the requirement of geologists, e.g., some channels may become disconnected during history-matching. Therefore, we propose a method of combining CDF-PCA together with streamline information. Because velocity of the tracer in the streamline provides connectivity information between injectors and producers, it enhances channel connectivity without over-correction on cell-based permeability during the process of history matching.

The CDF-PCA method is applied to a real-field case with three facies to quantify the quality of the models reconstructed. The history matching workflow is applied to a synthetic case. Our results show that the geological facies, reservoir properties, and production forecasts of models reconstructed with CDF-PCA are well consistent with those of the original models. The integrated HM workflow of CDF-PCA with streamline information generates reservoir models that honor production history with minimal compromise of geological realism.

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