Most attempts at developing diagenetic models (e.g., "reaction-path" approaches and their kin) typically ignore critical textural characteristics of sandstones and are based on theoretical arguments that are highly sensitive to uncertainties inherent in geologic settings. By contrast, while our approach does use a theoretical foundation, it incorporates empirically calibrated terms and specifically accounts for textural and compositional characteristics of sandstones. This approach has more in common with basin models than traditional thermodynamic/kinetic models.

We believe that computer models that incorporate the following characteristics have the greatest potential for accurate reservoir quality prediction in exploration and production settings and will be more cost effective to develop and apply than alternative approaches:

  • Model algorithms should have a theoretical basis but contain empirically calibrated terms.
  • The models should be tested rigorously by quantitative comparison of predictions with measurements from geologic datasets and diagenetic experiments.
  • We adopt accepted theory into our models only when it is consistent with data from geologic datasets and diagenetic experiments. When accepted theory does not pass muster then we undertake research to extend or revise theory.
  • The modeling system should incorporate Monte Carlo techniques to assess the uncertainty in model predictions that arises from uncertainties in input parameter values.