Performing look back studies to evaluate the economic and technical impacts of filed management decisions, is not a common occurrence in our industry. Even when such studies are performed, the results are hardly ever published for evaluation and scrutiny by the larger community of industry professionals. This paper presents such a study in the case of a mature giant oil field in the Middle East.

This prolific mature asset that includes more than 160 production wells has been the subject of peripheral water injection for many years to maintain pressure and help displace oil toward the production wells. Production was restricted to 1,500 bbls of fluid per day per well to avoid excessive water production as well as pulling oil from an over produced overlaying prolific reservoir. In 2005 a reservoir management study was commissioned to evaluate the impact of rate relaxation in this asset. The objective was to explore the likelihood of increasing oil production from the asset while minimizing the possibility of increasing the water cut.

The study was performed using the existing, history matched reservoir simulation model. To maximize the utility of the numerical model an AI-Based proxy model called Surrogate Reservoir Model (SRM) was developed and used for the study. Upon completion of the study (during the second quarter of 2005) the SRM ranked all the wells based on the probability of success1, once the rate relaxation program is implemented. In January 2006, management issued permission for the rates in 20 wells to be relaxed.

Using actual field data, this paper reports and evaluates the results and the consequences of the field management decisions, more than five years after their implementation. The approach and methodology used in this project will help reservoir and production managers, engineers and modelers make the most of the tools that are at their disposal to make more informed field management decisions. This paper demonstrates that using the right tools and strategies, skepticism about reservoir simulation models can be addressed effectively and can result in highly successful practices.

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