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Dynamic spatial and temporal prediction of changes in depositional processes on clastic shorelines: Toward improved subsurface uncertainty reduction and management
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  • journal_title:AAPG Bulletin
  • Contributor:R. Bruce Ainsworth ; Boyan K. Vakarelov ; Rachel A. Nanson
  • Publisher:American Association of Petroleum Geologists (AAPG)
  • Date:2011-02-01
  • Format:text/html
  • Language:en
  • Identifier:10.1306/06301010036
  • journal_abbrev:AAPG Bulletin
  • issn:0149-1423
  • volume:95
  • issue:2
  • firstpage:267
  • section:REGULAR ARTICLES
摘要

Existing classification schemes and models for clastic coastal depositional systems do not consider the potential amplifying or moderating effects of coastal morphology on depositional processes and do not provide a mechanism for the dynamic prediction of changes in coastal depositional style. A new process-based classification scheme based on the relative importance of primary, secondary, and tertiary processes is presented. This scheme permits a semiquantitative classification of clastic coastal depositional systems. In addition, it provides the basis for new models for clastic shorelines that convolve the effects of basin shape, coastal morphology, accommodation space, sediment supply, shoreline trajectory, and shelf width parameters on depositional processes. The end result is a marked improvement in the predictive capabilities of models. The models can describe and predict the likelihood of primary, secondary, and tertiary depositional processes acting in shoreline depositional environments via either a matrix or a decision tree approach. They are also dynamic in nature and can be applied to predict along-strike, updip, and downdip, or vertical changes in the dominance of depositional processes acting at any given location through geologic time. The key implications of these models are that given sets of known parameters, dominant and subordinate depositional processes or ranges of potential dominant and subordinate depositional processes acting at a coastline can be predicted. This provides an auditable methodology for determining reservoir modeling scenarios and reducing and managing the uncertainties in predictions of changes in clastic coastal depositional processes through time and space.

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