- [Voiceover] Briefly, let's talk about our rock physics modeling. What we've done is to model the Gassman and Krief geophysical equations in petrophysical terms. And what we can do is to create pseudo compressional and shear data from density and neutron measurements. And it has enormous applications. We're only going to look at one of them here, but it means that, in the absence of shear measurements, if you've correctly calibrated the reservoir, you can create pseudo shears that are obviously extraordinarily useful to geophysicists and engineers for applications they need. Also, we can look at fluid substitution. What happens if you replace gas by water? What effect will that have on the geophysical responses? And we can also look at what effect will it have on pressure reduction in a gas reservoir because the entropy and the neutron logs will indeed change and the acoustic logs will change and we can quantify that. Let's look at an example. This is an example from Kansas, again courtesy of Lyn Wadley. And what we've shown here is the raw data on the left and then the middle panel is the compressional data. The real data, the measured data, is in black, the pseudo data is in red. You can see the match is pretty good. Importantly, on the right is the shear data, and the shear data is incomplete. If you look carefully, the black is only recorded over certain intervals, and the intermediary things, and I don't know why, they didn't calculate the shear data. But we did, so you can see that you've then got a continuous curve of shear data that you can combine with a continuous curve of compressional data. Finally, what we are doing here is an approach of looking at porosity/saturation cross-plots to extend the buckles concept. Here is a schematic of what we're doing. On a log S-W, a log phi plot, a buckles plot, if you believe that phi times S-W-I is a constant, the slope of that line will be one. Where finding the slope isn't one, it can vary. But also, more importantly, you can recognize rocks, and if you, as the interpreter that does these choices, rocks that are higher quality, lower water saturation at any one porosity than poorer rocks. They may not be poorer rocks, and, again, this is ambiguous. It could be that they are the same quality rocks, but they got mobile water in them, and that's why they're falling off the irreducible water saturation line. Let's look at an example. This is the last, I believe. Here is an example from west Texas. This is San Andres at west Texas, and you can see that we've recognized red rocks with, similar to the previous example, low water compared with porosity as compared with blue rocks. So you'd predict that the blue rocks have poorer rock quality than the red, and, by golly, look, they do. It's a beautiful distinction. The red rocks have... This is a permeability/porosity core permeability cross-plot, and you can see it's distinguishing two different rock types, one higher quality than the other. I should say that this doesn't always work. You can't use this routinely, but we've done some recent work on the Bacan using ultra-high resolution data, 40 samples per foot, remarkably high. And what we're finding is that we are seeing a whole series of linear lines suggesting different rock types, not like this at all. There's no smearing. They are all discrete lines, all with about the same slope. And what we want to do is to see whether that correlates back to faces difference or something because we can identify exactly where they are to correlate that back to core data.