ORBi: Martin Roland - Using geostatistical constraints in electrical imaging for improved reservoir characterization

Reference : Using geostatistical constraints in electrical imaging for improved reservoir characteri...


	Document type :	Scientific congresses and symposiums : Unpublished conference
	Discipline(s) :	Engineering, computing & technology : Geological, petroleum & mining engineering
	To cite this reference:	http://hdl.handle.net/2268/80130

Title :	Using geostatistical constraints in electrical imaging for improved reservoir characterization
Language :	English
Alternative title :	[fr] Utilisation de contraintes géostatistiques en imagerie électrique pour améliorer la caractérisation des réservoirs
Author, co-author :	Martin, Roland [ > > ]
	Kemna, Andreas [ > > ]
	Hermans, Thomas [Université de Liège - ULg > Département Argenco : Secteur GEO3 > Géophysique appliquée >]
	Nguyen, Frédéric [Université de Liège - ULg > Département Argenco : Secteur GEO3 > Géophysique appliquée >]
	Vandenbohede, Alexander [ > > ]
	Lebbe, Luc [ > > ]
Publication date :	14-Dec-2010
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	American Geophysical Union Fall Meeting 2010
Event date :	du 13 décembre 2010 au 17 décembre 2010
Event organizer :	American Geophysical Union
Event place (city) :	San Francisco
Event country :	CA
Keywords :	[en] Geophysics ; Geostatistics ; Electrical imaging
Abstract :	[en] Developing predictive models of reservoirs is often complicated by the spatial heterogeneities and the different scales which control flow and transport processes. In numerous studies over the past two decades, geophysical imaging techniques have proved very useful for reservoir characterization. However, the loss of resolution and the non-uniqueness of standard solutions to inverse problems strongly limit the use of such deterministic imaging approaches. On the other hand, the use of common geostatistical approaches for reservoir characterization, for instance from logging information, may be a difficult task, since accurate variogram information is difficult to obtain (dense sampling in the vertical and lateral directions), and also because a high number of conditioned simulations is needed to remove statistical bias. Combining the high spatial sampling of deterministic geophysical imaging methods with geostatistical constraints, valid in the whole image plane, appears as a very promising approach to enhance reservoir characterization. To do so, we use a parameterized model covariance matrix based on standard variogram functions and a prior model as regularization operator in the inversion of electrical resistance data. This way of including additional data is not restricted to electrical data but the variogram parameters may be also inferred from for example available textural or lithological information. The benefit of the presented approach is twofold: (i) It honors the spatial statistics of the reservoir and (ii) it alters the posterior model by further reducing model ambiguity inherent to the inversion compared to classical (smooth model) regularization. The proof of concept is given by synthetic studies carried out on random fields from Gauss simulations with varying (an)isotropic scale lengths using different model (co)variogram functions. We also demonstrate the approach on electrical field data combined with borehole electromagnetic data from two artificial sea inlets in the nature reserve "The Westhoek" near the French-Belgian border. The electromagnetic logs were used to calculate an experimental vertical variogram characteristic of the study site. The results enabled to determine the extension of the salt water plume laterally, and significantly enhance its extension in depth, but also in terms of total dissolved solid content. These observations are in agreement with the hydrogeological situation at the site. A comparison with borehole data shows that the results are much more plausible than results obtained with a traditional smoothness constraint used as regularization operator. In conclusion, the incorporation of geostatistical information, vertical variograms in our case, in the inverse process improves imaging capabilities for reservoir characterization significantly.
Funders :	Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
Target :	Researchers ; Professionals
Permalink :	http://hdl.handle.net/2268/80130

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