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TitreUndiscovered petroleum accumulation mapping using model-based stochastic simulation
AuteurChen, Z; Osadetz, K G
SourceMathematical Geology vol. 38, no. 1, 2006 p. 1-16,
Séries alt.Secteur des sciences de la Terre, Contribution externe 2004404
ÉditeurSpringer Nature
Documentpublication en série
Mediapapier; en ligne; numérique
SNRC84E; 84F; 84K; 84L; 84M; 84N
Lat/Long OENS-120.0000 -116.0000 60.0000 57.0000
Sujetspétrole; exploration pétrolière; établissement de modèles; ressources; méthode de fourier
Illustrationsdigital images; equations; location maps; graphs
ProgrammeLa mise en valeur des ressources du Nord
ProgrammeLe Programme de recherche et de développement énergétiques (PRDE)
ProgrammeHydrates de gaz, Gestion des Hydrates de gaz
Diffusé2006 04 28
Résumé(disponible en anglais seulement)
Stochastic simulation has been proven to be a useful tool for revealing uncertainties in petroleum exploration
and exploitation. The application to petroleum resource assessment would result in predicted
potential accumulations with geographic locations, a desirable feature for improving both resource
management and exploration efficiency. The associated uncertainties with the prediction provide information
useful for exploration risk analysis. This attempt has been encumbered by two typical technical
difficulties: biased observation data and lack of information with respect to the undiscovered accumulation
locations. In this paper we propose a model-based simulation approach, in which models
are used to perform unbiased parameter estimation from biased data and to facilitate the location of
undiscovered petroleum accumulations based on reasoning of available geological and geophysical
observations. The Fourier transform algorithm is chosen for the simulation because the spatial correlation
and location-specific features can be studied separately from different data sources and integrated
in the simulation in a frequency domain. The proposed approach is illustrated by an example from
the Rainbow petroleum play in the West Canadian Sedimentary Basin. In the application example, a
pre-1994 exploration history data set was used as input, and the predictions are then checked against
the locations of post-1993 exploratory drilling results. The comparison of the predictions from the
proposed approach and the traditional conditional simulation shows that the model-based approach
captures the essentials of geological controls on the spatial distribution of petroleum accumulation,
thus improving the projections of undiscovered petroleum accumulations.