In this video, Luke explores the subject of getting geology out of the domain of algorithms and into the sphere of the human experts, the geologists!

Here's a still of this week's whiteboard. Top marks to Luke for artistic impression!

**TRANSCRIPTION:**

Hello, and welcome to another edition of Cognitive Geology. My name is Luke, and today we're following up on our previous video, where we talked about some of the challenges that can arise from using defaults in property modelling, by giving some ideas about how we could do better. Particularly what we're trying to do is to take back the geology; get it out of the hands of the algorithms and into the hands of the geologists.

### Algorithms don't model; geologists do

I'll start this off by just showing you one of the challenges that can arise if you use variograms to try to deliver your geological trends into the model. In doing so, we'll use for an example two coarsening-upward sequences, where we have two para-sequences of, say, a shoreface system coming through here, observed by these four wells A1 to A4. And let's see what would happen if we tried to distribute those purely with a variogram, that particular pattern.

### Variograms can invert real geological trends

So, if we did a variogram analysis and found that at some distance S we no longer have any information in the model from the observed points, what's going happen in a typical SGS-type implementation is once you get away from the well control, away from the distance of your variogram, the algorithm is going try to search to find a value to put there for you.

Now, if you are thinking along a K layer, as it's populating around the wells, it's consuming the data that it's observing in the nearby values out of the histogram. So, in the areas between it, it tends to fill it up with the other end of that histogram. And that, in this particular case, would result in a low value between these wells at that K layer, and vice versa at the bottom of the coarsening upward sequences, you might start to observe high values at that location.

So importantly, we can end up inverting that real geological trend if we just let the variogram be the only way of distributing that sequence. In doing so, you can end up with some significant problems from a flow perspective. You'd start to send the flow path in an elongated direction so that would delay water arrival time in your simulation, and increase your estimated recovery factor. So, you really could be setting yourself up there for some serious hurt when it comes to matching the real field results.

### "No trend" is still a trend

So, what should we do instead? We would advocate, here at Cognitive Geology, that you should be looking at describing the trends yourself explicitly in the models. This is where the geologist can try to instill their own scenarios of geological behaviours that they want to test, and see what impact that has on your economic result. So, an important suite that we often look for are things like depth trends, map-based trends, and sequence stratigraphic trends, and correlative relationships.

So, when we look at a particular vector - so something like porosity against depth - if we don't do an explicit model of analyzing that, we don't get away with it. We can't be the three wise monkeys and just ignore it, because no trend is still a trend in these types of property models. You are just invoking that there is no trend. It's a zero function as a relationship of that. So, if you want to say there is a degradation of porosity with depth, it's a good thing to draw that in there. It's a necessary thing.

Invoke your inner license, Y = MX + C...your artistic license, sorry. We don't get plus and minus infinity in any geological property. So, we tend to end up with shapes that are a little bit less functional to mathematics and a little bit more organic. So, feel free to describe that, particularly when you're looking at correlative relationships. Let's say this was acoustic impedance against the porosity estimate you might want to say, "Well, we have information from the acoustic impedance within some range, but at other ranges it becomes relatively uninformed."

Likewise with stratigraphy, we often describe a type well showing particular para-sequences that we expect to see in geology. You can invoke that in the K layer direction by describing those functions in the way that honours the well data, but implies the trends that you want it to be. Now, these can all be very uncertain, so you have a lot of degrees of freedom to try to map those out there so that you generate the scenarios that defend both the observed data, but also all the geological insight and any external self-control properties like your seismic attribute.

And just finally, one last point that we should address is that in a poorly sampled dataset - taking the example of a particular reservoir perhaps with a cluster of wells along the crest maybe one well down dip - sometimes the sequencing order that you choose in de-trending one after the other can actually result in a different 3D answer for the grid.

So, in this particular case, if we had this one well down here with a poorer quality reservoir, it wouldn't be clear from the observational data whether that was due to perhaps a depositional direction or a burial position at a relatively deeper location. So, if we started to try to understand what was in the north of this field, depending on what sequence we went through of de-trending of depth first and then the depositional direction or you can de-trend by depositional direction and then by depth, you can end up with quite different answers.

So, these are very powerful ways for you to invoke different property models and test scenarios. And, the important point is, get it into simulation as soon as you can, and see what impacts the bottom line, and what's controlling the flow behaviour of your reservoir.

I hope this was helpful - please let me know in the comments below. Thank you very much.