Abstract

Ganna field is a part of North Bahariya concession which is located in Abu El-Gharadig basin, Western Desert, Egypt. The field was discovered and started production in 2004 through the well Ganna-1 from Middle Abu Roash “G” reservoir. Five main reservoirs were tested and produced during the field life namely Abu Roash “E”, Upper Abu Roash “G”, Middle Abu Roash “G”, Lower Abu Roash “G”, and Upper Bahariya. A 3D reservoir simulation study was performed to aid in field future development.

Major challenges appeared during history matching process. The main challenge was the commingled production wells producing from multiple reservoirs with different characteristics. Even most of the static pressure points were measured commingle as well.

Post-production RFT data was the key utilized to calibrate the history match and reach the most accurate reservoir characterization for each reservoir individually. The history match strategy depended only on global modifications using the new commercialized optimization techniques.

Previously, model calibration or history matching has commonly been conducted on a single deterministic model by “manual” or “Trial and Error” approach. With recent advances in the application of uncertainty and optimization in numerical reservoir simulation it became a must to assess the effect of different parameters not only during history matching but also its effect on the forecast through producing a probabilistic forecasting (3). The feasibility of using the manual history matching with a single deterministic model has become questionable.

A Physically-sound proper set of parameters with realistic ranges are introduced at each stage of the process in logical order and introduced to optimizers to get the best history matched case with a possibility of considering more than one history matched case for a probabilistic forecasting scenarios (4).

This paper will present the approach utilized for calibrating the comingled production wells throughout post-production RFT data using the new uncertainty & optimization module to obtain the best global matched model.

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