Armed with high quality seismic and rock physics we can make quantitative predictions about reservoirs, fluids and pressures using seismic amplitudes. In an exploration setting, we believe that the safest method is to match AvO synthetics generated from a range of geologically plausible end-member scenarios to the seismic AvO response. The typical workflow is illustrated below, with the accompanying explanation for each panel:

  1. Use geological thinking to devise a limited subset of non-reservoir, reservoir and porefill scenarios that might account for an observed seismic amplitude response;
  2. Use well logs to constrain appropriate rock physics models for each lithology and porefill scenario;
  3. Build AvO synthetics for each scenario and update scenario probabilities based on the match to the observed AvO;
  4. Use AvO inversion to improve resolution and recast angle stacks for easier prospect screening and mapping over large areas guided by rock physics templates.

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We use a proprietary seismic petrophysics workflow to condition elastic well log data for rock physics analysis, during which the burial dependency of sediments and their porefill and pressure sensitivity is established. An appropriate rock physics model is then built to act as a constraint on QI predictions, often using the Rock Physics Template approach popularized by the Stanford University Rock Physics Lab. During this process, it is important to think about the microstructure of a rock and fluids within the pore-space and be mindful of subtle effects such as the grain lithologies, rock texture and pressures.

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The matching step (3) can be performed using the 2D synthetic modeling capabilities in some commercial software packages (and soon also within our INQUIREâ„¢ software). Calibration is achieved by scaling the synthetic to match a known brine sand, or perhaps by using a nearby marker event that is not expected to change laterally away from well control. When time and data quality permits, simultaneous AvO inversion or more sophisticated methods can be combined with the rock physics model to convert large datasets into (probable) rock and fluid properties, which in turn can be used to derive seismically-constrained static and dynamic reservoir models (this workflow is commonly referred to as reservoir characterization).

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