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Bridging multipoint statistics and truncated Gaussian fields for improved estimation of channelized reservoirs with ensemble methods
| Content Provider | Semantic Scholar |
|---|---|
| Author | Sebacher, Bogdan Stordal, Andreas S. Hanea, Remus |
| Copyright Year | 2015 |
| Abstract | In this paper, we present a new parameterization of channelized reservoirs with two facies types, which is coupled with the ensemble Kalman filter (EnKF) and the iterative adaptive Gaussian mixture filter (IAGM) for history matching (HM) of production data. The main objectives are to match the past data within the model and measurement uncertainties and to preserve the geological realism in order to predict the future behavior of the reservoir. The parameterization bridges the method of Gaussian truncation with multipoint statistics from a training image. To generate an ensemble of channelized reservoirs, a multi-point geostatistical tool (SNESIM) is used in combination with a training image. The parameterization is performed in a Gaussian space by drawing from a conditional Gaussian distribution with truncation rules estimated from the ensemble, ensuring that the updates are always facies realizations. The EnKF is a HM method that updates the ensemble based only on the first two statistical moments, which is not enough to characterize the posterior when channelized structures are present. Therefore, we propose using the iterative version of AGM in combination with SNESIM in the resampling step to better handle nonlinearities and preserve the channelized structure of the ensemble members. The results presented show that the IAGM procedure is able to reduce the uncertainty in the updated ensemble with realistic geological structure, in addition to having good predictability and preservation of channels. We performed a comparison with IAGM applied directly to the permeability field. |
| Starting Page | 341 |
| Ending Page | 369 |
| Page Count | 29 |
| File Format | PDF HTM / HTML |
| DOI | 10.1007/s10596-014-9466-3 |
| Volume Number | 19 |
| Alternate Webpage(s) | https://page-one.springer.com/pdf/preview/10.1007/s10596-014-9466-3 |
| Alternate Webpage(s) | https://doi.org/10.1007/s10596-014-9466-3 |
| Journal | Computational Geosciences |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
