- [Oliver] So there are two enabling developments. One is scientific and one is technological. The scientific development is shown here. Crude oils consist of gas, liquids and solids. And this means you might need some complexity in the equation state modeling because you have three phases not just two. The cubic equation of state, which is viewed by the reservoir engineering community as being a complete thermodynamics because that's the only one the petroleum scientists have given the reservoir engineers. Well, it's actually an equation of state that only works for gas-liquid. It was never designed for solids. There was a reason nobody had an equation of state for the solids. And that's because nobody knew the size of asphaltene particles. So it took us a long time, we solved that problem. The name of the model is, picked up the name Yen-Mullins Model. I didn't name it, otherwise it would be the Mullins-Yen Model. Professor Teh Fu Yen at University of Southern California you could say started modern asphaltene science in the 1960s. And so in the year 2010 I published this model, actually the year Professor Teh Fu Yen died. Yen-Mullins Model. It has the size of the molecules and the two aggregate species of the asphaltene's form in oil. Once you know the size you can solve Newton's second law, which is pretty fundamental. F equals m.g. You can solve Newton's second law of motion in a gravitational field. So that can now be done. That leads to this gravity term in the theory, the theory covering asphaltene gradients. There a couple other terms, entropy, which is a small term, so that's why it's written lightly here, and the solubility term. So it looks complicated, but actually it's a very simple theory. Flory-Huggins theory had just the entropy and solubility terms. Flory won the Nobel Prize, his theory's correct. We added the gravity term, and then I added Julian Zuo's name to the theory, our leading thermodynamicist. And what this equation gives is the ratio of color of optical density of oils in the reservoir if they're in thermodynamic equilibrium. So this takes us from log data directly to thermodynamic analysis immediately. When you put a model into the literature like this, which we did in 2010, it gets tested. The latest group to test our nanoscience model is the IBM imaging group, they're the world leaders in electro-imaging. The scientists at IBM Zurich won the Nobel Prize for developing the best techniques for molecular imaging, AFM/STM, and here we're showing results of the first high resolution images of molecules. There's a paper in a very high journal describing all these images of asphaltene molecules. You can see the six-membered carbon rings. It's amazing. And then you can see the electronic structure as well. This is imaging a specific electronic orbital. It's just amazing. I can tell you this is stunning. The paper states that they prove the main aspects of the Yens-Mullins model. So the methods we're talking about today are built on a very rock solid foundation. So this was the scientific development, the development of the first equation of state. And really the only one generally in use for asphaltene gradients, the Flory-Huggins-Zuo EOS. Well, that's the theory, but we also need to have the measurements. And the measurements of the gradients is best done with downhole fluid analysis. So that's what I'm showing here. And we introduced DFA formerly in about the year 2000, so it's been around a while. We're into our third generation tool, and it makes a lot of different measurements. But the measurement I'll focus on today is asphaltene content. But of course we always worry about things like contamination. Yes, we always worry about that. And there are a variety of different probes for minimizing contamination. And I would say in many of our cases, or most of our cases, we have very low contamination to worry about. I can address that in Q and A if anybody's interested. Okay, but we make all these different measurements, we rely heavily on the asphaltenes. There's a reason why we rely heavily on asphaltenes. I'm just putting this into a paper today actually. What we wanna do is measure gradients. And I'm showing a measure of gradients of asphaltene content as measured by DFA, downhole fluid analysis, against true vertical depth. So we can make these measurements. The resolving power, the ability to see one oil as being different from another, for DFA measurements is about 100,000, that's the resolving power. What that means, we can see about a hundred thousand different oils. We can resolve a hundred thousand oils. The human eye can resolve about 10 million colors. So actually even though a hundred thousand's pretty good, it's not as good as the human eye. If you look at the traditional method of looking at gradients in reservoirs, for example, by measuring thermal maturity markers, which we do, we love doing that, what we find is that the resolving power of that method is about 100. So the resolving power of the optics is approximately 1000 times more than the traditional way of measuring gradients. So it's much better to measure the asphaltenes for gradients. It's very valuable to have the geochemical information like the thermal maturity as well, as we'll see in this talk. But they each have their own place. This is an example of what you can do with the asphaltenes. So here's six sands, the operator one end hoping these were all connected. You can see by the pressures that we have different pressure regimes for the different sands. In addition, you can see that we have what we call asphaltene inversion, or really density inversions. The asphaltenes are dense, they don't float, they sink. If sands A and B were connected, then the asphaltenes would preferentially sink. There would be at least as much color in sample B as as sample A. We can see from the DFA data that's not the case. Again we have another density inversion, another density inversion. This is highly compartmentalized. In addition, the fluorescence measurement confirms that the color measurement is correct. The operator abandoned this reservoir. So this is the value of this data. They got out of this reservoir, this compartmentalized reservoir, without investing a lot of money and making big mistakes. Alright, but having this circumstance is a little bit like going to the doctor and saying, can you check my health. And he says, "Well, I might find cancer, I'll let you know if I see any." That's not very satisfying. What you would prefer the doctor to say is, "I can test, and you're cancer free." Well, that's what you want to hear about your reservoir, that the reservoir's cancer free, that it's connected, not that it's compartmentalized. So how do you do that? Well, this concept is very simple. Of course, people measure pressure. So if you have two different sands and two wells, whenever you check are the pressures and communication. Pressures are necessary but insufficient method of determining connectivity because two sands can be just weakly connected by horrible baffling yet be in the same pressure regime because it takes almost no mass transfer to equilibrate pressures. However, to equilibrate the fluids in a thermodynamic sense, it requires massive transfer of fluids throughout the reservoir. That requires good connectivity on a production time. So here's some examples with many different operators for many different fluid types. All employing the FHZ EOS. And these are the species from our nanoscience model. And if we're a condensate, we even are using the resin species, slightly smaller molecules. And the point is this, the solid lines are the FHZ prediction of thermodynamic equilibrium. And the points are the DFA measurements. And what we can see is that when these points line up with the theory, its asphaltenes are equilibrated, and it's connected reservoir. Here we have three stacked reservoirs, and they're individually connected. In all of these cases, the predictions that we made based on asphaltene equilibration were shown to be correct in production. And in the oil business, production is ground truth. So we have this method in wireline logging. We're establishing whether the asphaltenes are equilibrated. If they're equilibrated then we're predicting connectivity in most cases.