PGS hyperBeam combined with the flexibility of PGS hyperTomo enables fast-cycle cascaded updating for stable high-resolution model building. It can be extended via PGS hyperModel to fully automate model building.
- Fast testing of different trial model scenarios, capable of imaging multi-path arrivals and steep dips
- Can perform joint inversions for velocity and anisotropic parameters, include well constraints
- Instant availability of interpretation-ready QC attributes after each model update allows for close interaction between the interpreter and the geophysicist
- hyperModel automates velocity model building to achieve an accelerated project turnaround
- hyperModel mitigates the risk associated with target positioning and volumetrics and can be used directly in depth-migration, or in support of full waveform inversion (FWI)
Most velocity model building flows use a form of inversion, and both hyperBeam and hyperTomo use a platform based upon ray tracing and wavelets extracted from the data through a multi-dimensional dip scanning process.
West of Shetland. Resultant model from targeted tomographic updates within the volcanics (white arrows) and longer wavelength updates in the complex background model.
The award-winning hyperBeam is developed around three core technologies: visualization, model building, and workflow. Considerable flexibility exists within the steps for wavelet attribute selection, ray path discrimination and volumetric inversion masking. Options include ray path discrimination for salt modeling and smart flood migration for improved imaging and salt flank positioning. Model building can be done in the offset and angle domain, and has functionality to simultaneously build models for velocity, anisotropy and Q. In general terms, hyperTomo updating is performed to increasing depth and down to shorter wavelengths as we progress through the model building. hyperTomo provides extremely high-resolution updates due to the density of the picking.
hyperModel uses a Monte Carlo simulation, in numerous passes, to create a velocity model. When creating a model with an inversion the solution is generally underdetermined as not all the information is known to constrain the result to a single, correct solution. With hyperModel, we iteratively and globally solve a population of randomly sampled models as part of an optimization problem. With a statistics-driven feedback loop we automate the model building.
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