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TitleCombined inverse and forward numerical modelling for reconstruction of channel evolution and facies distributions in fluvial meander-belt deposits
 
AuthorParquer, M; Yan, N; Colombera, L; Mountney, N P; Collon, P; Caumon, G
SourceMarine and Petroleum Geology vol. 117, 104409, 2020 p. 1-15, https://doi.org/10.1016/j.marpetgeo.2020.104409 Open Access logo Open Access
Image
Year2020
Alt SeriesNatural Resources Canada, Contribution Series 20200058
PublisherElsevier
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf; html
Subjectssedimentology; stratigraphy; geophysics; Nature and Environment; Science and Technology; fluvial systems; fluvial deposits; channel deposits; meanders; computer simulations; facies models; modelling; seismic interpretations; seismic data; Methodology
Illustrationstables; geophysical images; geoscientific sketch maps; plots; seismic images; models; flow diagrams
Released2020 04 27
AbstractThe sedimentary record of meandering rivers contains a diverse and complex set of lithological heterogeneities, which impact natural resource management. Different methods exist to model such accumulated successions present in the subsurface by integrating knowledge of system evolutionary behaviour and geometries visible on seismic time or stratal slices. With reference to case-study examples, we review, discuss and employ two of these methods: (i) ChaRMigS generates possible scenarios for channel evolution and meander cut-offs by a reverse migration process; (ii) PB-SAND is a forward stratigraphic model which simulates fluvial point-bar geometry and facies distributions from known palaeo-channel geometries. We introduce a workflow to demonstrate how these two methods can be applied in combination to predict fluvial meander-belt facies distributions, using a subsurface dataset on a Pleistocene succession from the Gulf of Thailand where abandoned channels are visible on seismic time slices, but for which bar-accretion geometries and the exact timing of channel abandonment are unclear. Results show the value of a combined modelling approach to automate the stochastic generation of facies distributions constrained by seismic interpretations.
GEOSCAN ID326105

 
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