Abstract

Reservoir management requires tools that can (a) provide fast track and accurate assessment of a large variety of operations, while (b) are capable of quantifying uncertainties associated with management decisions. Reservoir managers must be able to compare and contrast a large number of development scenarios, while taking into account the uncertainties and risks involved with each scenario, in a relatively short period of time. To achieving this important task with traditional technologies one must either sacrifice the accuracy or the speed.

While numerical reservoir simulation models can provide the required accuracy, they fall short in providing the required speed. On the other hand, reduced models (conventional proxy models that rely on analytical solutions, simplified physics-based models or statistics-based response surfaces) can provide fast output (speed) but fail to fulfill the required accuracy.

Surrogate Reservoir Model (SRM) is a "smart" proxy of the numerical reservoir simulation model. SRM is developed to address this short coming in reservoir management. SRM takes advantage of the machine learning and pattern recognition capabilities of Artificial Intelligence and Data Mining (AI&DM) in order to "learn" and then accurately replicate the functionalities of the numerical reservoir simulation model. Smart proxy (SRM) runs at very high speed such that a single run of the smart proxy takes only a fraction of a second.

This paper presents highlights of development and application of a smart proxy (SRM) in the case of a giant mature oilfields in the United Arab Emirates. The SRM is developed for a multi-million cell, highly complex, naturally fractured, carbonate, numerical reservoir simulation model (developed using an industry standard commercial numerical simulator) with more than 500 wellbores. The SRM is validated through blind simulation runs and is used to plan filed development while honoring a number of operational constraints (such as limits on FBHP, GOR and WC) required by reservoir management team. SRM was used to increase field production without drilling new wells. This was accomplished by identify the optimum choke size schedule for each well in order to maximize oil production while minimizing the water cut.

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