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Thursday, March 16 • 4:00pm - 5:30pm
Poster: Estimation of Risk Measures under Parameter Uncertainty

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Many industrial applications, including reservoir modeling and management, are subject to uncertainty due to the lack of knowledge of model parameters. Estimating uncertainty in the quantities of interest (such as oil production, water cut, pressure) of the systems subject to random variables (such as reservoir equations with uncertainties in the geological parameters) is a challenging task due to the large computational effort involved with simulations of numerical models. Particular difficulties arise when information is needed regarding the tail statistics of the random quantities of interest, such as in risk-averse oil production optimization.

We consider a class of risk measures that assign a value to the overall hazard associated with unfavorable outcomes of the random quantities of interest (e.g., large losses in profit). Common risk measures, such as absolute semideviation or conditional value-at-risk, are difficult to estimate accurately due to the sampling deficiencies. We propose an approach to estimation of these risk measures that relies on their analytic properties, and statistical concepts, such as importance sampling. Our approach aims to reduce the number of samples required to estimate risk measures accurately, thus leading to the reduction of number of costly simulations. The efficiency of our approach is demonstrated on the problem related to the steady flow in porous media.


Thursday March 16, 2017 4:00pm - 5:30pm
Exhibit Hall BRC

Attendees (1)