Understanding the linkage between grain mineralogy, sedimentary processes, and depositional facies enhances the reliability of petrophysical interpretations based on rock typing methods. Conversely, formation evaluation methods relying solely on petrophysical rock typing expose serious limitations when predicting ultimate reservoir performance in a large number of wells. The reconciliation between depositional and petrophysical facies along with diagenetic studies using thin-section petrography provides a way to understand the various processes affecting producing reservoirs and to better predict their deliverability.
Petrophysical models improve when porosity-permeability trends are interpreted within the context of depositional and diagenetic processes. Distinctive trends that are not evident from simple routine core data or standalone log-derived solutions become apparent. Petrographic point-count analysis from well-defined depositional facies reveals the diagenetic evolution of the porosity-permeability relationships.
The development of robust log absolute permeability models is a critical step in any tight gas petrophysics workflow. The orders of magnitude in permeability that might characterize a single porosity value exponentially increase uncertainty. The use of mathematical algorithms such as neural networks and principal component analysis commonly does not work because available input logs are only sensitive to lithology and porosity. Understanding the depositional and diagenetic controls on pore geometry allows the derivation of properties needed for narrowing the uncertainty in the absolute permeability assessment. We use multi detector pulsed neutron tools in capture and activation modes to process proxies for porosity and quartz content in framework grains.
In summary, depositional facies and diagenetic controls on pore geometry need to be analyzed in porosity and permeability space and correlated with hydraulic rock types. This paper presents an example from the Upper Cretaceous Almond Formation in the Greater Green River Basin, USA which shows how the uncertainty in reservoir performance is reduced when performing an integrated depositional, diagenetic, and petrophysics rock type analysis coupled with special processing of cased-hole log suites.
