Stochastic structural modelling in sparse data situations

Abstract : This paper introduces a stochastic structural modelling method that honours interpretations of both faults and stratigraphic horizons on maps and cross-sections in conjunction with prior information, such as fault orientation and statistical size-displacement relationships. The generated stochastic models sample not only geometric uncertainty but also topological uncertainty about the fault network. Faults are simulated sequentially; at each step, fault traces are randomly chosen to constrain a fault surface in order to obtain consistent fault geometry and displacement profile. For each simulated fault network, stratigraphic modelling is performed to honour interpreted horizons using an implicit approach. Geometrical uncertainty on stratigraphic horizons can then be simulated by adding a correlated random noise to the stratigraphic scalar field. This strategy automatically maintains the continuity between faults and horizons. The method is applied to a Middle East field where stochastic structural models are generated from interpreted two-dimensional (2D) seismic lines, first by representing only stratigraphic uncertainty and then by adding uncertainty about the fault network. These two scenarios are compared in terms of gross rock volume (GRV) uncertainty and show a significant increase in GRV uncertainty when fault uncertainties are considered. This underlines the key role of faults in resource estimation uncertainties and advocates a more systematic fault uncertainty consideration in subsurface studies, especially in settings in which the data are sparse.
Document type :
Journal articles
Complete list of metadatas

Cited literature [43 references]  Display  Hide  Download

https://hal.univ-lorraine.fr/hal-01276852
Contributor : Georessources Ul <>
Submitted on : Wednesday, April 26, 2017 - 12:09:07 PM
Last modification on : Friday, April 5, 2019 - 8:21:39 PM
Long-term archiving on : Thursday, July 27, 2017 - 12:46:05 PM

File

CherpeauCaumon_PGRevFinal_HAL....
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution - ShareAlike 4.0 International License

Identifiers

Citation

Nicolas Cherpeau, Guillaume Caumon. Stochastic structural modelling in sparse data situations. Petroleum Geoscience, Geological Society, 2015, 21 (4), pp.233-247. ⟨10.1144/petgeo2013-030⟩. ⟨hal-01276852⟩

Share

Metrics

Record views

340

Files downloads

377