History matching of the Norne Field using the Ensemble based Reservoir Tool (EnKF/ES)
In this thesis two stochastic algorithms, the Ensemble Smoother (ES) and the Ensem-ble Kalman Filter (EnKF), have been utilized as automatic history matching meth-ods through the Statoil developed program the Ensemble based Reservoir Tool. Sta-toil, through the IO Center at NTNU, provided real histo...
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Institutt for petroleumsteknologi og anvendt geofysikk
2014
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ftntnutrondheimi:oai:ntnuopen.ntnu.no:11250/240311 2023-05-15T17:25:05+02:00 History matching of the Norne Field using the Ensemble based Reservoir Tool (EnKF/ES) Solheim, Daniel Aleksander Kleppe, Jon Rwechungura, Richard Norges teknisk-naturvitenskapelige universitet, Fakultet for ingeniørvitenskap og teknologi, Institutt for petroleumsteknologi og anvendt geofysikk 2014 http://hdl.handle.net/11250/240311 eng eng Institutt for petroleumsteknologi og anvendt geofysikk 733897 ntnudaim:10483 http://hdl.handle.net/11250/240311 119 Master thesis 2014 ftntnutrondheimi 2019-09-17T06:48:54Z In this thesis two stochastic algorithms, the Ensemble Smoother (ES) and the Ensem-ble Kalman Filter (EnKF), have been utilized as automatic history matching meth-ods through the Statoil developed program the Ensemble based Reservoir Tool. Sta-toil, through the IO Center at NTNU, provided real historical production and pressuredata. These data was used to condition on a parametrized ECLIPSE reservoir modelof the Norne field. Parameters conditioned on includes the field parameters porosity,i-permeability, net-to-gross, and z-direction transmissibility multiplier, as well as fault multipliers, region transmissibility multipliers, minimum pore volumes and relative permeability endscaling options. The E-segment model and the Full-field model were both studied. With an ensemble size of 120 realizations for the E-Segment model and 80 realizations for the Full-field model, both algorithms performed well. In most of the results an initial high uncertainty in the prior provided the necessary coverage of the historical observed data. Overall the EnKF performed better than the ES, which is natural comparing time and computational power required. Some spurious correlations and one particular ensemble collapse were experienced. The EnKF-analysed ensemble production rates provides a lot less uncertainty than the initial ensemble, and for most plots a slightly better match than the reference case. The EnKF-algorithm is shown to be vulnerable to small ensemble sizes versus large amounts of conditioning parameters, as well as highly correlated historical observations. Master Thesis Norne field NTNU Open Archive (Norwegian University of Science and Technology) |
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Open Polar |
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NTNU Open Archive (Norwegian University of Science and Technology) |
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ftntnutrondheimi |
language |
English |
description |
In this thesis two stochastic algorithms, the Ensemble Smoother (ES) and the Ensem-ble Kalman Filter (EnKF), have been utilized as automatic history matching meth-ods through the Statoil developed program the Ensemble based Reservoir Tool. Sta-toil, through the IO Center at NTNU, provided real historical production and pressuredata. These data was used to condition on a parametrized ECLIPSE reservoir modelof the Norne field. Parameters conditioned on includes the field parameters porosity,i-permeability, net-to-gross, and z-direction transmissibility multiplier, as well as fault multipliers, region transmissibility multipliers, minimum pore volumes and relative permeability endscaling options. The E-segment model and the Full-field model were both studied. With an ensemble size of 120 realizations for the E-Segment model and 80 realizations for the Full-field model, both algorithms performed well. In most of the results an initial high uncertainty in the prior provided the necessary coverage of the historical observed data. Overall the EnKF performed better than the ES, which is natural comparing time and computational power required. Some spurious correlations and one particular ensemble collapse were experienced. The EnKF-analysed ensemble production rates provides a lot less uncertainty than the initial ensemble, and for most plots a slightly better match than the reference case. The EnKF-algorithm is shown to be vulnerable to small ensemble sizes versus large amounts of conditioning parameters, as well as highly correlated historical observations. |
author2 |
Kleppe, Jon Rwechungura, Richard Norges teknisk-naturvitenskapelige universitet, Fakultet for ingeniørvitenskap og teknologi, Institutt for petroleumsteknologi og anvendt geofysikk |
format |
Master Thesis |
author |
Solheim, Daniel Aleksander |
spellingShingle |
Solheim, Daniel Aleksander History matching of the Norne Field using the Ensemble based Reservoir Tool (EnKF/ES) |
author_facet |
Solheim, Daniel Aleksander |
author_sort |
Solheim, Daniel Aleksander |
title |
History matching of the Norne Field using the Ensemble based Reservoir Tool (EnKF/ES) |
title_short |
History matching of the Norne Field using the Ensemble based Reservoir Tool (EnKF/ES) |
title_full |
History matching of the Norne Field using the Ensemble based Reservoir Tool (EnKF/ES) |
title_fullStr |
History matching of the Norne Field using the Ensemble based Reservoir Tool (EnKF/ES) |
title_full_unstemmed |
History matching of the Norne Field using the Ensemble based Reservoir Tool (EnKF/ES) |
title_sort |
history matching of the norne field using the ensemble based reservoir tool (enkf/es) |
publisher |
Institutt for petroleumsteknologi og anvendt geofysikk |
publishDate |
2014 |
url |
http://hdl.handle.net/11250/240311 |
genre |
Norne field |
genre_facet |
Norne field |
op_source |
119 |
op_relation |
733897 ntnudaim:10483 http://hdl.handle.net/11250/240311 |
_version_ |
1766116391412301824 |