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Most of the mature fields in the United States have been producing for many years. Production in these fields started at a time when reservoir characterization was not a priority; therefore they lack data that can help in reservoir characterization. On the other hand to re-vitalize these fields in a time that price of hydrocarbon is high, requires certain degree of reservoir characterization in order to identify locations with potentials of economical production. The most common type of data that may be found in many of the mature fields is production data. This is due to the fact that usually production data is recorded as a regulatory obligation or simply because it was needed to perform economic analysis.In this paper the methodology is introduced and its application to a field in the mid-continent is demonstrated.

Coalbed methane is becoming one of the major natural gas resources. CO 2 injection into CBM reservoirs is used as an effective method for CBM production enhancement (ECBM) and for long term sequestration of CO 2 (CO 2 Seq). Reservoir simulation is used regularly for building representative ECBM and CO 2 Seq models. Given the wide range of uncertainties that are associated with the geological models (that forms the foundation of any reservoir simulation), comprehensive analysis and uncertainty quantification of ECBM and CO 2 Seq models become very time consuming if not impossible. This paper addresses the uncertainty quantification of a complex ECBM reservoir model. We use a new technique by developing a Surrogate Reservoir Model (SRM) that can accurately mimic the behavior of the commercial reservoir model. Upon validation of SRM, we perform Monte Carlo Simulation (MCS) in order to quantify the uncertainties associated with the geological (CBM) model. Performing MCS requires thousands of simulation runs that can be performed easily once the SRM is developed. Key Performance Indicators (KPI) of the simulation model are identified to help reservoir engineers concentrate on the most influential parameters on the model's output when studying the reservoir and performing uncertainty analysis. Unlike conventional geo-statistical techniques that require hundreds of runs to build a response surface or a proxy model, building an SRM only requires a few simulation runs.

Most of the mature fields in the United States have been producing for many years. Production in these fields started at a time when reservoir characterization was not a priority; therefore they lack data that can help in reservoir characterization. On the other hand to re-vitalize these fields in a time that price of hydrocarbon is high, requires certain degree of reservoir characterization in order to identify locations with potentials of economical production. The most common type of data that may be found in many of the mature fields is production data. This is due to the fact that usually production data is recorded as a regulatory obligation or simply because it was needed to perform economic analysis.In this paper the methodology is introduced and its application to a field in the mid-continent is demonstrated.