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Abstract

The ultimate goal in any petroleum, natural gas or geothermal reservoir engineering study is to make performance predictions. Some of the variables to be predicted are pressures, recoverable oil, recoverable gas, recoverable heat from geothermal reservoirs, water cut, gas oil ratio and etc. Making predictions is vital for the economical exploitation of the resources. What is more important is the quantification of the uncertainty related to the predictions. Uncertainty in all future predictions is inherent due to (i) measurement errors or noise in the data, (ii) lack of data, (iii) modeling errors, (iv) span of the available observed data and (v) the non-linear relationship between the data and the model response. In this study we present an overview of the methodologies used to quantify the uncertainty in future predictions from oil, gas and geothermal reservoirs. We will present synthetic applications of various techniques for quantifying the uncertainty of gas in place for gas reservoirs, of heat in place for geothermal reservoirs, of water cut from oil wells and of pressure and temperature predictions using tank models for geothermal reservoirs. The techniques discussed will cover the Monte Carlo method, the analytical uncertainty propagation equation, the gradual deformation method, the randomized maximum likelihood method and the more recent Ensemble Kalman Filter method.

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/content/papers/10.3997/2214-4609-pdb.377.117
2011-05-11
2024-03-28
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609-pdb.377.117
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