Print Email Facebook Twitter Ensemble-based conditioning of reservoir models to seismic data Title Ensemble-based conditioning of reservoir models to seismic data Author Leeuwenburgh, O. Brouwer, J. Trani, M. Faculty Civil Engineering and Geosciences Department Geotechnology Date 2010-09-17 Abstract While 3D seismic has been the basis for geological model building for a long time, time-lapse seismic has primarily been used in a qualitative manner to assist in monitoring reservoir behavior. With the growing acceptance of assisted history matching methods has come an equally rising interest in incorporating 3D or time-lapse seismic data into the history matching process in a more quantitative manner. The common approach in recent studies has been to invert the seismic data to elastic or to dynamic reservoir properties, typically acoustic impedance or saturation changes. Here we consider the use of both 3D and time-lapse seismic amplitude data based on a forward modeling approach that does not require any inversion in the traditional sense. Advantages of such an approach may be better estimation and treatment of model and measurement errors, the combination of two inversion steps into one by removing the explicit inversion to state space variables, and more consistent dependence on the validity of assumptions underlying the inversion process. In this paper, we introduce this approach with the use of an assisted history matching method in mind. Two ensemble-based methods, the ensemble Kalman filter and the ensemble randomized maximum likelihood method, are used to investigate issues arising from the use of seismic amplitude data, and possible solutions are presented. Experiments with a 3D synthetic reservoir model show that additional information on the distribution of reservoir fluids, and on rock properties such as porosity and permeability, can be extracted from the seismic data. The role for localization and iterative methods are discussed in detail. Subject assisted history matchingensemble Kalman filterseismic history matchingensemble randomized maximum likelihood filter To reference this document use: http://resolver.tudelft.nl/uuid:54e32db9-3dd8-4c31-bafc-8d38cf4f7c66 Publisher Springer ISSN 1420-0597 Source http://www.springerlink.com/content/86462w6314t12384/ Source Computational Geosciences, 15 (2), 2011 Part of collection Institutional Repository Document type journal article Rights (c) 2010 Springer Science+Business Media B.V. Files PDF leeuwenburgh.pdf 3.66 MB Close viewer /islandora/object/uuid:54e32db9-3dd8-4c31-bafc-8d38cf4f7c66/datastream/OBJ/view