On the influence of stratigraphic correlation uncertainty in sedimentation rate determination - A training-based method

Jonathan Edwards and Florent Lallier and Guillaume Caumon and Cedric Carpentier and Julien Charreau. ( 2016 )
in: 2016 RING Meeting, ASGA

Abstract

We discuss the sampling and the volumetric impact of stratigraphic correlation uncertainties in basins and reservoirs. From an input set of wells and an analog stratigraphic model we evaluate the probability for two stratigraphic units to be associated. The resulting correlations are then used to create 3D stratigraphic models. We apply this method on a set of synthetic wells sampling a forward model built with Dionisos. To perform cross-validation of the method, we introduce a distance comparing the relative geological time of two models for each geographic position (u; v). We present some consistent results, showing that increasing the number of wells tends to decrease the distance between the models and also decreases the standard deviation. Finally, the models are compared in terms of volumes, and we discuss the impact of such differences.

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BibTeX Reference

@INPROCEEDINGS{,
    author = { Edwards, Jonathan and Lallier, Florent and Caumon, Guillaume and Carpentier, Cedric and Charreau, Julien },
     title = { On the influence of stratigraphic correlation uncertainty in sedimentation rate determination - A training-based method },
 booktitle = { 2016 RING Meeting },
      year = { 2016 },
 publisher = { ASGA },
  abstract = { We discuss the sampling and the volumetric impact of stratigraphic correlation uncertainties in
basins and reservoirs. From an input set of wells and an analog stratigraphic model we evaluate
the probability for two stratigraphic units to be associated. The resulting correlations are then
used to create 3D stratigraphic models. We apply this method on a set of synthetic wells sampling
a forward model built with Dionisos. To perform cross-validation of the method, we introduce a
distance comparing the relative geological time of two models for each geographic position (u; v).
We present some consistent results, showing that increasing the number of wells tends to decrease
the distance between the models and also decreases the standard deviation. Finally, the models are
compared in terms of volumes, and we discuss the impact of such differences. }
}