Let’s Get Technical, Technical – Criteria for Facies / Rock Types in Subsurface Modeling

Michael Pyrcz, PhD, P.Eng (daytum Founding Advisor)

After the last lengthy post, this one is short, sweet, [slightly] technical, and vintage…

As you can imagine, Facies / Rock type is an important decision for subsurface modeling. Below are a few comments, perspectives, and criteria on the topic.

Comments and Perspectives:
– When contemplating facies, the process should remain collaborative and integrate the holistic expertise from the project team (Geologists, Reservoir Modelers, Reservoir Engineers, Petrophysicsts and Geophysicists)

– Facies / Rock types must improve subsurface prediction away from the data or they do not add value!

– The number of facies is a balancing act between accuracy of geological concepts, statistical inference, and modeling efforts

Criterion Considerations Example
Separation of Rock Properties Facies must divide the properties of interest that impact subsurface environmental and economic performance (e.g. grade, porosity and permeability)
Identifiable in Data Facies must be identifiable with the most common data available.  (e.g. facies identifiable only in cores are not useful if most wells have only logs)
Map-able Away from Data Facies must be easier to predict away from data than the rock properties of interest directly.  Facies improves prediction.
Sufficient Sampling There must be enough data to allow for reliable inference of reliable statistics for rock properties for each facies

When Michael is not building python packages or mentoring students, he’s either running, out on his Jeep, or kayaking around Lake Austin. You can find him on Twitter here, and his YouTube channel here.

Criteria for Facies / Rock Types in Subsurface Modeling

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