Water flooding is an established method to increase oil production. In this research, we present a novel approach that uses data mining techniques on the operations data on a complex mature oil field located in the Gulf of Suez, currently being water flooded. We show how such methods help improve reservoir characterization for this specific field is particularly challenging because of its geological complexity and field performance.

The continuous recording of production and injection data presents a new opportunity to apply analytical approaches to reservoir management. Such approaches provide an alternative to the traditional history-match model update and prediction that is not only time-consuming but also carry forwards all subsurface uncertainties.

A combination of qualitative (cross-correlation analysis) and quantitative analysis (capacitance resistance model) is used to obtain an overall waterflood injection strategy for this Gulf of Suez field. In this manuscript, we focus on the analysis obtained from cross-correlation analysis. The presented analysis helps identify connectivity between wells in the reservoir during waterflood. The method presented is also adapted to specific characteristics of this field - water drive production in this model.

We present evidence of how salinity data can be used to further justify the linkages between the different wells obtained from the cross-correlation analysis. We also show comparison between results from this analytical technique and the streamline approach. This comparison with salinity and streamlines helps benchmark the model results especially in cases where such secondary data (salinity/streamlines) are not available. The results presented in this research can be adapted to any waterflooded field to optimize recovery at frequent intervals, where injection and production data is continuously available.

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