logo scube2 SCube is a SKUA-GOCAD plugin to perform stochastic multi-well correlations.

With a set of rules, it computes the cost of the association of each pair of units on two different wells and then outputs the correlation of the wells with the least expensive associations.

The rules that can be chosen are various such as the size of the units, the depth, the facies,... or also the frequence of observation of the association in a training forward model.

It can be used for multi-well correlation and it is possible to automatically build stratigraphic grids from these correlations. Since 2018, it is being gradually replaced by the stand-alone code WeCo

 

 StratigraphicUncertainty

This animation, generated with SCube, shows a few realizations of stochastic stratigraphic correlation on simple passive margin model.Correlation rules between wells are statistically calibrated from a process-based stratigraphic model.

This highlights theambiguity that exist in stratigraphic correlations, and show the impact this may have on subsurface grids anchored on boreholes.

The full methodology is explained in {publi 4813 Edwards et al (2018)}.

 

The SCube plugin can be tested with the training (available to sponsors).

Main publications:

Edwards et al. (2018). Uncertainty management in stratigraphic well correlation and stratigraphic architectures: A training-based method. Computers & Geosciences, 111 (11-17)

Lallier et al. (2016). Uncertainty assessment in the stratigraphic well correlation of a carbonate ramp: Method and application to the Beausset Basin, SE France. Comptes-Rendus Geoscience, 348:7 (499-509)

Lallier et al (2012). Relevance of the stochastic stratigraphic well correlation approach for the study of complex carbonate settings: application to the Malampaya buildup (Offshore Palawan, Philippines). Geological Society of London, Special Publication 370 (265-275)

Contact: Jonathan Edwards.

Download (for sponsors):

https://trainings.ring-team.org/WeCo/index.html

Papers using SCube:

Yoon et al., EAGE 2017