Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Temistocles Simon Rojas"'
Publikováno v:
Mathematical Geosciences. 51:209-240
Bayesian uncertainty quantification of reservoir prediction is a significant area of ongoing research, with the major effort focussed on estimating the likelihood. However, the prior definition, which is equally as important in the Bayesian context a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::47f38d5ebb4125a0a43217a45f04b080
https://doi.org/10.1007/s11004-018-9774-6
https://doi.org/10.1007/s11004-018-9774-6
Publikováno v:
Mathematical Geosciences. 51:241-264
Models used for reservoir prediction are subject to various types of uncertainty, and interpretational uncertainty is one of the most difficult to quantify due to the subjective nature of creating different scenarios of the geology and due to the dif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d168edc982777dc70266d6257b4ed251
https://doi.org/10.1007/s11004-018-9755-9
https://doi.org/10.1007/s11004-018-9755-9
Autor:
Sebastian Geiger, Patrick William Michael Corbett, Daniel Arnold, D. Tatum, Vasily Demyanov, Michael Andrew Christie, Temistocles Simon Rojas
Publikováno v:
Computers & Geosciences. 50:4-15
Benchmark problems have been generated to test a number of issues related to predicting reservoir behaviour (e.g. Floris et al., 2001, Christie and Blunt, 2001, Peters et al., 2010). However, such cases are usually focused on a particular aspect of t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::82bad55ba243031a34042c7525afbc5a
https://doi.org/10.1016/j.cageo.2012.09.011
https://doi.org/10.1016/j.cageo.2012.09.011
Autor:
Vasily Demyanov, L. Buckhouse, Daniel Arnold, Temistocles Simon Rojas, Michael Andrew Christie
Publikováno v:
Proceedings.
We propose a novel approach to evolve the model through the update process based on the ensemble of possible model realisations that are fused together in a data driven way rather than assimilated under certain assumptions. Multiple Kernel Learning (
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1922f33fe132756ff6d46cd599e3688f
https://doi.org/10.3997/2214-4609.20147473
https://doi.org/10.3997/2214-4609.20147473
History Matching Deltaic Reservoir Models Controlled by Realistic Sedimentological Prior Information
Publikováno v:
Proceedings.
The generation of multiple reservoir models that match production data is one of the advantages of automatic history matching (AMH). Including facies geometry variations within the AHM process without the modeller control, could result in the selecti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1fdb92ec52789fa8e996686a4f5b875c
https://doi.org/10.3997/2214-4609.20141508
https://doi.org/10.3997/2214-4609.20141508
Publikováno v:
Lecture Notes in Earth System Sciences ISBN: 9783642324079
Accounting for geological scenario uncertainty is one of the contemporary challenges in reservoir prediction modelling. Multi-point statistics approach allows distinguishing between different geological scenarios represented by various training image
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dbe242ff76c6311524e9e3357ae667b1
https://doi.org/10.1007/978-3-642-32408-6_35
https://doi.org/10.1007/978-3-642-32408-6_35
Publikováno v:
Scopus-Elsevier
One of the challenges in automatic history matching is to ensure the preservation of the realistic geological features of reservoir models. Realism of the history matched model is vital because of its impact on the flow response and the reliability o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c754deaa29d487cfb4ec604f8bfd3d5
http://www.scopus.com/inward/record.url?eid=2-s2.0-84930467297&partnerID=MN8TOARS
http://www.scopus.com/inward/record.url?eid=2-s2.0-84930467297&partnerID=MN8TOARS