Uncertainty management in stratigraphic well correlation and stratigraphic architectures: A training-based method

Jonathan Edwards and Florent Lallier and Guillaume Caumon and Cedric Carpentier. ( 2018 )
in: Computers & Geosciences, 111 (11-17)

Abstract

We discuss the sampling and the volumetric impact of stratigraphic correlation uncertainties in basins and reservoirs. From an input set of wells, we evaluate the probability for two stratigraphic units to be associated using an analog stratigraphic model. In the presence of multiple wells, this method sequentially updates a stratigraphic column defining the stratigraphic layering for each possible set of realizations. The resulting correlations are then used to create stratigraphic grids in three dimensions. We apply this method on a set of synthetic wells sampling a forward stratigraphic 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, and we compare the models in terms of volumes. Results show the ability of the method to automatically generate stratigraphic correlation scenarios, and also highlight some challenges when sampling stratigraphic uncertainties from multiple wells.

Download / Links

BibTeX Reference

@ARTICLE{Edwards,
    author = { Edwards, Jonathan and Lallier, Florent and Caumon, Guillaume and Carpentier, Cedric },
     title = { Uncertainty management in stratigraphic well correlation and stratigraphic architectures: A training-based method },
     month = { "feb" },
   journal = { Computers & Geosciences },
    volume = { 111 },
      year = { 2018 },
     pages = { 11-17 },
       doi = { 10.1016/j.cageo.2017.10.008 },
  abstract = { We discuss the sampling and the volumetric impact of stratigraphic correlation uncertainties in basins and reservoirs. From an input set of wells, we evaluate the probability for two stratigraphic units to be associated using an analog stratigraphic model. In the presence of multiple wells, this method sequentially updates a stratigraphic column defining the stratigraphic layering for each possible set of realizations. The resulting correlations are then used to create stratigraphic grids in three dimensions. We apply this method on a set of synthetic wells sampling a forward stratigraphic 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, and we compare the models in terms of volumes. Results show the ability of the method to automatically generate stratigraphic correlation scenarios, and also highlight some challenges when sampling stratigraphic uncertainties from multiple wells. }
}