This international webinar describes how multi-attribute seismic analysis is applied using the Paradise® software to visualize thin beds and facies below classical seismic tuning thickness. The material is presented by Mr. Rocky Roden, an industry thought leader and Senior Consulting Geophysicist for Geophysical Insights. Hal Green, Director of Marketing for Geophysical Insights, introduces the topic and presenter.
Our visualization is certainly better than it has been. But do we still pick peaks, troughs, and zero crossings; fundamentally do we do the same thing from an interpretation perspective? I want you to think about this as we go through this presentation.
The conventional definition of tuning thickness or vertical resolution as indicated by Sheriff in his dictionary, is a bed that is a quarter wavelength in thickness for which reflections from the top and the bottom, interfere. This interference pattern is constructive where the contrasts of the two interfaces are of opposite polarity. Of course this produces very strong amplitude.
Well, many years ago, Meckel and Nath, Neidell and Poggiagliolmi, and Schramm et al, will use this amplitude below tuning, this normalized amplitude below tuning, and scale that to come up with some sort of a thickness estimate based on scaling this information. These approaches have limitations and require assumptions that may not be met all the time. For example, these assumptions assume that the thickness is the only thing that is happening below tuning and that no reservoir parameters or properties are changing. Which may or may not be accurate. So what we are going to talk about is a multi-attribute approach, which uses numerous seismic attributes that exhibit below tuning effects. So when we combine these in a machine learning approach, we can get a better idea, a more accurate depiction of thin beds.
Robertson and Nogami back in 1984 looked at a wedge model and asked what do you have below tuning that created an increase in frequency, and in fact they called it frequency tuning. With instantaneous frequency, there are some things going on when you get below tuning. Now, Zeng, Radovich, Oliveros, and Hardage took advantage of what they call frequency spikes. What this is, is when the instantaneous frequency has a hard time computing because of very thin beds and/or some sort of constructive or interference patterns.
Now, Taner took advantage of this and he developed what he called a thin bed indicator, which is just the difference between the instantaneous and the time-averaged frequencies. Zing identified when you did a 90 degrees rotated wavelet, which is quite similar to a Hilbert Transform, and you apply that to a wedge model, when you get to the tuning thickness or below, it's easier to understand the thickness of those beds below tuning.
The SOM, or the self-organizing maps, employ the values from these different attributes and looks at the patterns and clusters that they form. We look at these on a sample-by-sample basis, which is very important, very significant in this overall process. What this allows us to do is to do a thin bed analysis and we're not hampered by the traditional frequency and amplitude of the wavelet resolution limitations.
So one of the principals in self-organizing maps that enable us to try to see thin beds is that it is unsupervised. SOM's are an artificial neural network employing unsupervised learning methods. That's very important that it is unsupervised. There is no previous training on anything ahead of time.
A SOM is nothing but a cluster or a pattern recognition approach. So what it does is it looks at all the information from these attributes and something we call attribute space. It identifies any sorts of natural patterns or clusters that develop. Again, we identify these different patterns or clusters with something we call neurons. The reason we do this is that these patterns or clusters, depending on the seismic attributes that you're employing, have geologic significance. We are seeing tremendous detail with these SOM analyses and this thin bed analysis. If we can look at this with numerous attributes, then we can start to see thin beds that are not limited by the conventional thinking of amplitudes and frequencies.