Copyright 2006, Society of Petroleum Engineers This paper was prepared for presentation at the 2006 Abu Dhabi International Petroleum Exhibition and Conference held in Abu Dhabi, U.A.E., 5–8 November 2006. This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented. Write Librarian, SPE, P.O. Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435. Abstract Simulation models are routinely used as a powerful tool for reservoir management. The underlying static models are the result of integrated efforts that usually includes the latest geophysical, geological and petrophysical measurements and interpretations. As such, these models carry an inherent degree of uncertainty. Typical uncertainty analysis techniques require many realizations and runs of the reservoir simulation model. In this day and age, as reservoir models are getting larger and more complicated, making hundreds or sometimes thousands of simulation runs can put considerable strain on the resources of an asset team, and most of the times are simply impractical. Analysis of these uncertainties and their effects on well performance using a new and efficient technique is the subject of this paper. The analysis has been performed on a giant oil field in the Middle East using a surrogate reservoir model. The surrogate reservoir model that runs and provides results in real-time is developed to mimic the capabilities of a full field simulation model that includes one million grid blocks and takes 10 hours to run using a cluster of twelve 3.2 GHz CPUs. In order to effectively demonstrate the robustness of Surrogate Reservoir Models and their capabilities as tools that can be used for uncertainty analysis, one must demonstrate that SRMs are competent in providing reasonably accurate results for multiple realizations of the reservoir being studied. In order to demonstrate such robustness and their predictive capabilities as well as their limitations, this paper will examine the performance of the surrogate reservoir models on different geologic realizations of the static model. Introduction In two previous SPE papers some of the aspects of the Surrogate Reservoir Models were discussed. In the first paper1 the idea of Surrogate Reservoir Models was introduced and in the second paper2 its application in quantifying uncertainties associated with a reservoir simulation study was explored. The conventional approach for uncertainty analysis in our industry is mainly based on geostatistics. One such method that is often used is Response Surfaces3-5. Response Surfaces are statistical interpolations (based on fitting some type of pre-determined models – linear or quadratic –) of model responses to different geological, geophysical and petro-physical realizations6,7. Another method that has been used more in other industries is called the Reduced Model. Reduced Models are approximations of full three dimensional numerical simulation models that essentially approach an analytical model for tractability8. One of the major advantages of Surrogate Reservoir Models, when compared to conventional geostatistical techniques, is the small number of simulation runs that is required for their development. For example, instead of hundreds of simulation runs that would be required to perform a SPE 101474 Uncertainty Analysis of a Giant Oil Field in the Middle East Using Surrogate Reservoir Model Shahab D. Mohaghegh, West Virginia U. & Intelligent Solutions, Inc., Hafez Hafez, ADCO, Razi Gaskari, WVU, Masoud Haajizadeh, ADCO and Maher Kenawy, ADCO2 Uncertainty Analysis of a Giant Oil Field in the Middle East Using Surrogate Reservoir Model SPE 101474 limited geostatistical study, development of the Surrogate Reservoir Model that is the subject of this paper required only 10 simulation runs. On the other hand, the capabilities of the SRM for analyses are more far reaching than the alternative technique that required hundreds of runs. The reason for being able to do much more with a limited number of simulations runs (about 10 runs as appose to hundreds and sometimes thousands of runs in the case of some geo-statistical analyses) has to do with the efficiency by which SRMs use the resources offered by each simulation run. This efficiency is associated with the way Surrogate Reservoir Models represent the reservoir. The objective of this paper is to demonstrate the approach that is taken by Surrogate Reservoir Models in representation of multiple realizations within a single simulation run. In order to clearly demonstrate this aspect of SRMs, which is a key component in their development, an introduction on the philosophical approach used by the SRM is appropriate. Surrogate Reservoir Models are prototypes of complete three dimensional numerical reservoir models that are capable of accurately mimicking the behavior of the full field models with all their details and complexity. The word “prototype” is used here in the context of the prototype theory that is defined as “a model of graded categorization, where all members of a category do not have equal status.” This definition becomes clearer once the development process of Surrogate Reservoir Model is considered1. Given the above definition of Surrogate Reservoir Model, as a prototype of the full field model, the approach used during the development of the Surrogate Reservoir Models fits more appropriately within the approach summarized in the system theory9 (depicted in Figure 1) rather than the approach commonly used in our industry that is essentially based on