Issue |
EPJ Web of Conferences
Volume 6, 2010
ICEM 14 – 14th International Conference on Experimental Mechanics
|
|
---|---|---|
Article Number | 22022 | |
Number of page(s) | 7 | |
Section | Soils and Geomaterials | |
DOI | https://doi.org/10.1051/epjconf/20100622022 | |
Published online | 10 June 2010 |
https://doi.org/10.1051/epjconf/20100622022
Imaging pore space in tight gas sandstone reservoir: insights from broad ion beam cross-sectioning
1
Structural geology, Tectonics and Geomechanics, RWTH Aachen
University, Lochnerstrasse 4-20, D-52056
Aachen,
Germany
2
Institut für Geowissenschaften, Johannes Gutenberg-Universität
Mainz, Mainz, Germany
3
Wintershall Holding AG, Erdölwerke Barnstorf,
Rechterner Straße
2, 49406
Barnstorf,
Germany
4
Geological Institute, RWTH Aachen University,
Wüllnerstr. 2,
D-52056
Aachen,
Germany
5
Wintershall Holding AG, Friedrich-Ebert-Straße 160,
34119
Kassel,
Germany
a e-mail: g.desbois@ged.rwth-aachen.de
Monetization of tight gas reservoirs, which contain significant gas reserves world-wide, represents a challenge for the entire oil and gas industry. The development of new technologies to enhance tight gas reservoir productivity is strongly dependent on an improved understanding of the rock properties and especially the pore framework. Numerous methods are now available to characterize sandstone cores. However, the pore space characterization at pore scale remains difficult due to the fine pore size and delicate sample preparation, and has thus been mostly indirectly inferred until now. Here we propose a new method of ultra high-resolution petrography combining high resolution SEM and argon ion beam cross sectioning (BIB, Broad Ion Beam) which prepares smooth and damage free surfaces. We demonstrate this method using the example of Permian (Rotliegend) age tight gas sandstone core samples. The combination of Ar-beam cross-sectioning facility and high-resolution SEM imaging has the potential to result in a step change in the understanding of pore geometries, in terms of its morphology, spatial distribution and evolution based on the generation of unprecedented image quality and resolution enhancing the predictive reliability of image analysis.
© Owned by the authors, published by EDP Sciences, 2010
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