Proxy Comparison for Sorting Models and Assessing Uncertainty on Oil Recovery Profiles

Gaetan Bardy and Pierre Biver and Guillaume Caumon and Philippe Renard and Vincent Corpel and Peter R. King. ( 2014 )
in: ECMOR XIV, EAGE, Catania

Abstract

To study the impact of subsurface uncertainties on oil recovery, it is common to build a large set of models which cover these uncertainties. Despite increase of computational capabilities, as models become more complex, it is not possible to perform full physic flow simulation for all the generated models. This is why stochastic reservoir model sets are often decimated to assess the impact of static uncertainties on dynamic reservoir performance. This contribution will focus on the use of proxy to perform this data set reduction. A lot of different proxies have been developed, from the simplest to the more complicated so it is difficult to choose the good one according to a particular goal. We present different criteria to compare the proxy quality and their helps to assess uncertainties on oil recovery. A first criterion will be based on the relation which may exists between the model distances computed on the proxy responses and those compute on flow responses. Another criterion is the speed factor and simplification provide by the proxy compared to the full physic simulator. These two criteria are very simple and can be applied in an early time to avoid deploying time consuming proxies which won’t provide accurate information. The last criterion presented here, is the confidence intervals which can be computed around probabilistic reservoir production forecasts computed on a small representative subset of model. Even if this criterion can be used only when the entire dataset has been simulated, it provides some quantification about a possible bias created by a proxy and the remaining uncertainties on oil recovery. We present here a comparison study between widely different proxy responses applied on a real dataset of that methodology. This will give us some keys to choose a proxy which is a good compromise between accuracy and easy to handle methodology.

Download / Links

BibTeX Reference

@INPROCEEDINGS{,
    author = { Bardy, Gaetan and Biver, Pierre and Caumon, Guillaume and Renard, Philippe and Corpel, Vincent and King, Peter R. },
     title = { Proxy Comparison for Sorting Models and Assessing Uncertainty on Oil Recovery Profiles },
 booktitle = { ECMOR XIV },
      year = { 2014 },
  location = { Catania },
organization = { EAGE },
       doi = { 10.3997/2214-4609.20141901 },
  abstract = { To study the impact of subsurface uncertainties on oil recovery, it is common to build a large set of models which cover these uncertainties. Despite increase of computational capabilities, as models become more complex, it is not possible to perform full physic flow simulation for all the generated models. This is why stochastic reservoir model sets are often decimated to assess the impact of static uncertainties on dynamic reservoir performance. This contribution will focus on the use of proxy to perform this data set reduction. A lot of different proxies have been developed, from the simplest to the more complicated so it is difficult to choose the good one according to a particular goal. We present different criteria to compare the proxy quality and their helps to assess uncertainties on oil recovery. A first criterion will be based on the relation which may exists between the model distances computed on the proxy responses and those compute on flow responses. Another criterion is the speed factor and simplification provide by the proxy compared to the full physic simulator. These two criteria are very simple and can be applied in an early time to avoid deploying time consuming proxies which won’t provide accurate information. The last criterion presented here, is the confidence intervals which can be computed around probabilistic reservoir production forecasts computed on a small representative subset of model. Even if this criterion can be used only when the entire dataset has been simulated, it provides some quantification about a possible bias created by a proxy and the remaining uncertainties on oil recovery. We present here a comparison study between widely different proxy responses applied on a real dataset of that methodology. This will give us some keys to choose a proxy which is a good compromise between accuracy and easy to handle methodology. }
}