Coalbed methane is becoming one of the major natural gas resources. CO2 injection into CBM reservoirs is used as an effective method for CBM production enhancement (ECBM) and for long term sequestration of CO2 (CO2Seq). Reservoir simulation is used regularly for building representative ECBM and CO2Seq 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 CO2Seq 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.


Reservoir simulation provides information on the behavior of the reservoir under various production and/or injection scenarios. Reservoir engineers and managers use reservoir simulators to better understand the reservoir, perform future performance predictions and uncertainty analysis. Because of non-uniqueness of simulation models and uncertainties associated with the geo-cellular model (reservoir parameters), uncertainty analysis becomes an important task that is required for making operational decisions, since such decision making process necessitates the quantification of model uncertainties.

Different techniques are used to quantify the uncertainties associated with reservoir parameters. MCS is a technique that is widely used in the oil and gas industry for the purpose of uncertainty analysis. Since MCS uses a statistical representation of parameters being studied, it requires thousands of reservoir realizations in order to provide a meaningful (statistically representative) conclusion on the effect of uncertain parameters on the model's performance. Generating thousands of simulation models especially in case of large and complex models, which could take a long time to make a single simulation run, is impractical. Attempts have been made to perform uncertainty analysis with as small number of realizations as possible. Common techniques that have gained popularity in the oil and gas industry are the Experimental Design technique and Reduced Models. Response Surface Models are generated in order to analyze the results obtained from Experimental Design.

Experimental Design has been used in reservoir simulation since 1990s. It is used to get maximum information at the lowest experimental cost, by changing all the uncertain parameters simultaneously. It is essentially an equation derived from all the multiple regressions of all the main parameters that affect the reservoir's response (1). Many studies have shown that by using the Experimental Design the reservoir model still needs to be run hundreds of times.

Reduced Models are approximations of full three dimensional numerical simulation models that approach an analytical model for tractability (2).

This paper presents the application of a recently developed technique for reservoir simulation and modeling, called Surrogate Reservoir Modeling (SRM), to model and analyze an enhanced coalbed methane project. The CBM reservoir used in this analysis is a synthetic reservoir with characteristics representative of a coal in the Appalachian Basin. All the reservoir simulation is performed using a commercial reservoir simulator (3).

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