Luke is back at the Cognitive Whiteboard again for the second video in our series on facies - this week he looks at data analysis in facies modelling.

Below is a still image of this week's whiteboard, for your further perusal!

# TRANSCRIPTION

# Removing The Blindfold: Taking Control in Facies Analysis

Hello! Welcome back to The Cognitive Whiteboard. My name's Luke and today, we're filming the second video around a series on facies modelling. In this video we're going to focus in on how we do the data analysis behind the facies model. It's arguably one of the most difficult parts of the workflow and so it's a place to take your time and get it right.

### First things first

Really, before we get into any of the analysis of this, we should have had the conversation with the stratigrapher, the geophysicist, and all of the rest of the geologists in your region to understand what your depositional environment is likely to be in your particular area, and make sure that you understand how the best practices reflect that kind of depositional system. And the job that we're going to do as modellers is going be to implement that theory in three dimensional space.

A common starting point is to look at the vertical proportions that we see in the wells and this isn't an easy task to do by any means. It's not easy for a couple of reasons. Firstly, we're sampling a discrete property, and we're sampling it usually with a handful of wells and so it's quite difficult to see a lot of character in that. We have essentially thrown away a lot of information, we made the choice of bidding a bunch of rock types together into a particular facies class. So it's important that we take that observational data, tie it back into the depositional concept and produce a conceptual vertical curve that mirrors what you're trying to invoke inside the model.

### Worthwhile extra work

Now, I, personally, am not smart enough to get the right curve for a full field in one go. Quite frankly, I don't know what the right vertical proportion curve would be for that model there in that grid, because it varies across space. Essentially, over here, it's 100% of the orange facies and over here, there's none of it. So what's the right vertical proportion curve? It really depends upon the grid structure as well as the depositional model. So I find a much simpler routine is to actually generate more than one vertical curve across my model, I do this manually, and then draw some polylines and use a little bit of mathematics to blend them together and construct a combined vertical proportion concept, essentially a three dimensional model now that blends together both my map-based theory and my vertical proportion curves. It's a little bit more work but I find it gives me a lot more control.

And then of course, it's very important that we see if we can drive something out of the seismic to give us an insight into the reservoirs. When we do so, just, of course, be aware of the vertical resolution that you get from seismic. There may be a lot of tuning effects. This reservoir is thinning off to the left here so I will be quite skeptical about what I'm seeing here. And of course, the seismic wavelet may be several times thicker than the reservoir target so it's important to understand: is this an average effect over the whole reservoir? Is it a particular stratigraphic zone, because any seismic image sees not only the target of interest but also the rock adjacent to that.

And of course, the seismic interpreter, the geophysicist, is going to be seeing the same rocks binned in a different way. They're going to be binning it by acoustic properties and the sedimentologist might be binning it by other methods. So it's quite important, referring back to the first video, that we have that clear and calm conversation so that we can relate these properties to the facies that we have observed in the wells and bring it all together.

### Adding mathematics

And finally, that point of bringing it together, most tools - when we come to execute the facies modelling routines - allow you to blend more than one trend into essentially a single prior that will go into the geostatistical routine. Most of the time, commercial software will give you essentially slider bars of these various kinds of properties which are essentially saying “I love” or “I hate” this property, it works or it doesn't work, I'm going to try to blend this together. Arguably - maybe because that's quite easy to control mathematically in an uncertainty workflow at the end - I'm not a big fan of this because I don't get to see that outcome in three dimensional space before it goes into the modelling routine. So again, it's relatively simple mathematics: a couple scripts, and you can end up combining these together yourself and have a nice three dimensional concept that you get to QC yourself before it goes into the modelling routine.

I hope this is all helpful. We use this kind of concepts in the next video and start talking about executing specific routines.