Faults play an important role in recharging many geothermal reservoirs, and seismic information can image the locations of these faults. Sensors such as DAS have the potential to have both higher temporal and spatial resolution than geophones. The value of information (VOI) metric is used toobjectively quantify the value of the single-component horizontal DAS, compared to more sparsely-spaced geophones, and using both receivers together. This methodology quantifies the accuracy of fault locations in seismic migration images when horizontal fibers, geophones and a combination of the two are used to construct 2D migration images. A machine learning approach is used to calculate the posterior probabilities needed for the value of imperfect information. Our 2D numerical experiments compare images created from both sparsely space (80m), two-component geophone sampling to high spatial resolution (1 m), single-component DAS. For images created from vertical source, DAS performed relatively better (F1=0.919) than geophones (F1=0.877); this was also true for a horizontal source, but the difference in F1 scores was reduced: DAS 0.939 and geophones 0.931. Our transferrable methodology can provide guidance on which scenarios DAS can successfully improve images of important structures in the subsurface, and presents an efficient method for obtaining reliability statistics.
Presentation Date: Monday, October 12, 2020
Session Start Time: 1:50 PM
Presentation Time: 4:20 PM
Presentation Type: Oral