Calibration improves uncertainty quantification in production forecasting
| Autor: | W. John Lee, Martin G. Alvarado, Duane A. McVay |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Calibration (statistics)
Weather forecasting Geology Sample (statistics) computer.software_genre Fuel Technology Geochemistry and Petrology Earth and Planetary Sciences (miscellaneous) Econometrics Performance prediction Economic Geology Sensitivity analysis Uncertainty quantification computer Reliability (statistics) Uncertainty analysis |
| Zdroj: | Petroleum Geoscience. 11:195-202 |
| ISSN: | 2041-496X 1354-0793 |
| Popis: | Despite recent advances in uncertainty quantification, the petroleum industry continues to underestimate the uncertainties associated with reservoir production forecasts. This paper describes a calibration process that can improve quantification of uncertainties associated with reservoir performance prediction. Existing methods underestimate uncertainty because they fail to account for all, and particularly unknown, factors affecting reservoir performance and because they do not investigate all combinations of reservoir parameter values. However, the primary limitation of existing methods is that their reliability cannot be verified because the testing of an estimate of uncertainty from existing methods yields only one sample for what is inherently a statistical result. Verification and improvement of uncertainty estimates can be achieved with calibration – comparison of actual performance with previous uncertainty estimates and then using the results to scale subsequent uncertainty estimates. Calibration of uncertainty estimates can be achieved with a more frequent, if not continuous, process of data acquisition, model calibration, model prediction and uncertainty assessment, similar to the process employed in weather forecasting. Improved ability to quantify production forecast uncertainty should result in better investment decision making and, ultimately, increased profitability. |
| Databáze: | OpenAIRE |
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