--> Abstract: Bridging the Information Gap: Qualitative Fracture Prediction from Seismic Data, by Peter O. Thierer, H. Trappe, H. Endres, T. Lohr, C.M. Krawczyk, O. Oncken, D.C. Tanner, and P.A. Kukla; #90072 (2007)

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Bridging the Information Gap: Qualitative Fracture Prediction from Seismic Data

Peter O. Thierer1, H. Trappe1, H. Endres1, T. Lohr2, C.M. Krawczyk2, O. Oncken2, D.C. Tanner3, and P.A. Kukla4
1TEEC, Isernhagen, Germany
2GeoForschungsZentrum, Potsdam, Germany
3University Goettingen, 37077 Goettingen, Germany
4RWTH Aachen, 52056 Aachen, Germany

This study is part of a DFG fundet case study where different disciplines worked together improving the knowledge of the developement of the North German Basin. We focus the area of a producing gas field with special respect to the analysis of seismic and sub-seismic deformation. This requires a very detailed fault detection in order to bridge the information gap between seismic data and well data.
Besides advanced coherency algorithms as e.g. Structural Entropy and Shaded Relief that take into account local dip and azimuth distribution of coherent energy, we present IHS-color displays for more detailed fault detection. With the help of these methods, additional tectonic lineaments were detected, thus revealing a higher degree of fault density than could have been inferred from conventional amplitude images.
In addition to fracture attributes like "fault density by count", length or connectivity, we show the attribute Fractal Dimension (FD), which is an index for the relation of long to short fractures. Results, FD and residuals, are displayed in a map showing the areal distribution of the attribute.
Correlating FMI/FMS Log Data and Fractal Dimension values from seismic data shows a linear relation between both. When using this relation, we end up with a predictive FMI-map showing the spatial distribution of fracturation for undrilled areas on FMI scale. This predictive attribute map is completely based on the seismic data.
We show that integrating borehole images, core studies, geostatistical results and seismic data can significantly help geoscientists gain a better understanding of complex structures.


AAPG Search and Discovery Article #90072 © 2007 AAPG and AAPG European Region Conference, Athens, Greece