UNDERSTANDING DEPOSITIONAL AND DIAGENETIC PROCESSES IMPROVE PETROPHYSICAL ROCK TYPING WORKFLOWS IN TIGHT GAS RESERVOIRS

摘要

Understanding the linkage between grain mineralogy,sedimentary processes, and depositional faciesenhances the reliability of petrophysical interpretationsbased on rock typing methods. Conversely, formationevaluation methods relying solely on petrophysical rocktyping expose serious limitations when predictingultimate reservoir performance in a large number ofwells. The reconciliation between depositional andpetrophysical facies along with diagenetic studies usingthin-section petrography provides a way to understandthe various processes affecting producing reservoirs andto better predict their deliverability.Petrophysical models improve when porositypermeabilitytrends are interpreted within the context ofdepositional and diagenetic processes. Distinctivetrends that are not evident from simple routine core dataor standalone log-derived solutions become apparent.Petrographic point-count analysis from well-defineddepositional facies reveals the diagenetic evolution ofthe porosity-permeability relationships.The development of robust log absolute permeabilitymodels is a critical step in any tight gas petrophysicsworkflow. The orders of magnitude in permeability thatmight characterize a single porosity value exponentiallyincrease uncertainty. The use of mathematicalalgorithms such as neural networks and principalcomponent analysis commonly does not work becauseavailable input logs are only sensitive to lithology andporosity. Understanding the depositional and diageneticcontrols on pore geometry allows the derivation ofproperties needed for narrowing the uncertainty in theabsolute permeability assessment. We use multidetector pulsed neutron tools in capture and activationmodes to process proxies for porosity and quartzcontent in framework grains.In summary, depositional facies and diagenetic controlson pore geometry need to be analyzed in porosity andpermeability space and correlated with hydraulic rocktypes. This paper presents an example from the UpperCretaceous Almond Formation in the Greater GreenRiver Basin, USA which shows how the uncertainty inreservoir performance is reduced when performing anintegrated depositional, diagenetic, and petrophysicsrock type analysis coupled with special processing ofcased-hole log suites.