The goal of this project is to develop a next-generation fracture detection and characterization technology for producing natural gas from low permeability formations.
The research proposed here combines a new seismic shear wave (s-wave) imaging concept for 3-1 acquisition geometries with a new microfracture based analysis technique of oriented sidewall cores. This is the next-generation technology for detecting and characterizing subsurface fractures. The seismic component of this research is an approach that abandons the conventional industry practice of using Alford rotation to create fracture-sensitive s-wave images in 3-D geometries. Our investigation of existing industry practice leads us to conclude that data processing techniques, that separate s- waves into fast and slow modes in 3-D geometries, are fundamentally flawed. We propose that a new data- processing model, based on SH and SV mode concepts, be used in 3-D imaging of s-waves. This model is leading us to a new data-processing technology for detecting fractures when s-waves are recorded by 3-1 seismic templates. The seismic calibration portion of the research relies on collecting sidewall cores and then observing and classifying micro-fractures to calibrate fracture-sensitive seismic attributes.
Performer: University of Texas at Austin Bureau of Economic Geology
Austin, Texas 78713
This research used a new seismic shear-wave (s-wave) imaging concept for 3-D acquisition geometries for detecting and characterizing subsurface fractures. An unexpected change in an industry partner resulted in no core being available for microfracture studies. A new data-processing model based on SH and SV mode concepts were used for 3-D imaging of shear waves. Seismic data acquired across a fractured carbonate reservoir system illustrate how 3 component 3-D seismic data can provide useful information about fracture systems. Fast-S and slow-S data are used to illustrate how these effects can be analyzed in the prestack domain to recognize fracture azimuth, and then demonstrate how fast-S and slow-S data volumes can be analyzed in the post-stack domain to estimate fracture intensity.
The key observations from the study were:
Project Start: June 6, 2000
Project End: December 31, 2003
DOE Contribution: $599,367
Performer Contribution: $170,800
NETL – Gary Sames (412-386-5067 or firstname.lastname@example.org)
UTA – Robert Hardage (512-471-1534 or email@example.com)
Final Report - [PDF-6050KB]