Meeting the gas demand-supply imbalance is crucial in addressing energy requirements for the future. An increasing percentage of the supply of gas will be sourced from unconventional gas reservoirs, for instance, tight sands/shales and coal bed methane reservoirs. However, while unconventional resources contribute an ever increasing fraction of the total gas supply, an integrated analysis and characterization of these reservoirs is often difficult owing to uncertainties associated with various parameters in reservoir and geologic description. Moreover, the high cost of data acquisition limits the amount of information available to build reservoir models.

The main goal of this paper is to provide a methodology for integrated assessment of the petrophysical, geologic and reservoir parameter uncertainties to provide decision makers a rational basis for managing uncertainties leading to improved decisions and resource management. The workflow involves identifying key parameters influencing uncertainties in recovery predictions through an Experimental Design/Response Surface methodology. The need to do a global sensitivity analysis motivates the workflow presented in our study.

The relevant parameters are used to build an emulator or an analog of the reservoir which is then used to rapidly provide performance predictions, taking into account the uncertainties associated with the parameters. The entire framework is applied to a case study to illustrate the cost and time benefits of such an approach. The significant contribution of such an analysis is identifying the effects on reservoir performance due to uncertainties associated with characterizing the reservoir leading to an informed and rational decision making process with significant cost savings and a reduction in computational effort.

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