- [Steve] It started out with a case study in heavy oil sandstones. It was a beautiful case, a beautiful situation to explore this because we had very high porosity sands, very high permeability sands, and there were eight stacked sands within approximately an 800-foot interval, I believe. They were very similar. They were all in the same geologic formation. But the difference was there was a variation in oil gravity from 8 to 15. Exploring this, I put together a matrix where I took snapshots of just the sandstone part of the log and clipped out all the shales and then put them all on so the equivalent sands were next to each other. I had a matrix seven wells wide and eight sands tall. It gave me the ability to really explore what is this quick look showing. The operator had just recently purchased this field. They were expecting that the oil gravity was consistent across the field, and permeability was consistent across the field, and that the oil gravity varied from top to bottom in a linear way. In doing this, we were able to observe that some parts of the field have better movability, better permeability, or better API gravity than others, and that individual sands have got better oil gravity than others. It gave granularity to the data in this field for this operator and it enabled them to better complete the zones. It gave them a lot more information. It gave new insight into the reservoir, the field they had just purchased. It verified the movability application. Then I was exploring diatomite formations. For those of you that are not familiar with diatomite, I have a chunk of it right here that Jim brought. It's a very unique reservoir rock in California and around the Pacific Rim that is very low density. You notice right away that for its size it's very light. It's composed of diatoms, which is a form of algae which flourished in the Miocene period, flourished in great numbers, and then died in great numbers. So we have large accumulations. Some of the diatomite wells have up to 2,000 feet of this diatomite section. It's very high porosity because the skeletal structure of the diatoms is preserved. Another term for it is opal-A, if you're familiar with diatomite. It has high porosity, 70% porosity. You can have oil saturations as high as 70%, but perms very low. In some cases where there's diagenesis to the opal-CT phase you do get natural fractures. The brittleness increases and there's some fracturing. You can have a large variety, or a large variation in oil gravity, from 8 to 30 API. It's all over California, and there is estimated 10 billion barrels of oil in place. The petrophysical aspect of diatomite is it's fairly easy with dielectric techniques to identify oil saturation, but it's near impossible to identify the higher productive zones within the diatomite section. It causes quite a problem where two wells look identical to each other from a log perspective, and one produces three times what the other does. As well, when you have a 2,000-foot section and you're trying to figure out where to complete, where to focus your completion activities and your stimulations, it's very difficult to figure that out, until we've come to this technique. In a high porosity but low perm environment, you would think that the movability would have very little change between dielectric currents. That's what you typically see. In this case we have, now you can check the porosity scale out, 15 to 75, and the density curve is touching 75 up here, the neutron and high porosity are very statistical. Here are the four dielectric curves. Here's the resistivity. It's pretty low value and not much fluctuation. This has an average. This is a light oil in Beldridge Field, Kern County, with 27 API gravity oil, low perm, high porosity. The water filled porosity from the dielectric is reading on average about 40%. The total porosity, based on the density or density neutron is 70%. The water saturation is just the ratio, 57%. That's the beauty of the dielectric, is it's fairly straightforward to get oil saturation. Just compare the two curves proportional separation. The new analysis available from this quick look, we've got four curves here and we can see that they're overlayed, which means the oil will not move, or has not moved in the evasion process. This is at around 1,000 feet. It gets down to 1,130 right there. Go a little bit deeper, down to 1,230-1,240. Now we see a zone that has got an anomalously high movability compared to the zones above and below it. In this case, it's happening right where there's kind of a density change, and it's possible that there's some of this change from opal-A to opal-CT across here. In fact, many times in diatomite you see the development of movability right where there is some sort of change, but not always. Here is diatomite from a different field and a different county, and this is heavy oil diatomite. Now we've seen that you can have movability in the diatomite and now we'll take a look at what significance it might have. Here we've got a similar, now we've got a 0 to 85 porosity in the scale, and lots of separation through here. You go through the same calculation. We have 47% average water saturation, or 53% oil saturation, with an average porosity of 75%, which is a lot of oil if you go through the reserve calculations. Again you see that the dielectric curves are overlayed. Now, this is a heavy oil field, where the gravity is, on average, 14 for the field, this particular well was 13. In these heavy oil fields they use steam to stimulate the well and get the oil to flow. This uses cyclic steam where once the well is logged and completed and cemented, and they give it time to fully cure, then they perforated it and inject steam, let the steam soak for a while, and then produce what they can and then continue to do more steam cycles like that. After stable production, this well is making 16 barrels of oil a day, which if you go through the calculations and see how much oil is actually there, that's a pretty low rate. When we compare that to another well in the field, in the same field but at the other end of it, we can see that now we've got lots of indications of movability on the dielectric. In this case, the well makes 44 barrels of oil a day, a little bit higher gravity on this well. This is actually a look back that I did with this operator. They'd been drilling for a while. They've been running the new tool. But as many clients in California, they're really just interested in one depth investigation dielectric, because that's what all their previous interpretations were based on. They have huge databases, and that's what they wanted to use. But when I used Jim's algorithms and we ran through this movability quick look, then I went to this operator and I said, "Hey, do you want to try it on your wells?" They said, "Yes, because we have a puzzling situation. "We've got wells that look identical in the logs, "but at this end of the field they only "produce 15 barrels a day, and over here "they produce 40 or even 50. "Can you explore it with the technique "and see what you come up with." It was pretty straightforward that when you compare the wells, and there were multiple wells with similar responses, you can correlate from well to well the same loads where they have the movability to get better oil production. They believed that this movability in this rock was due to fracturing. This is one of the early examples that I came up with about the productivity prediction. Working with another operator, they had a similar situation where they ran a new tool, weren't using the data, and did their completions based on the old processes, the old techniques, that they had developed over time. Here are three wells. Their geologist did this study. Here we've got total porosity versus the single-depth investigation dielectric. You can see kind of similar responses. This is the perforated interval. Each of these wells, they perforate at 20 feet, and selected that 20 feet in the same method they had used before, which had more to do with positioning of the reservoir. They wanted to start low and work their way up. They didn't have any other data, so they just started at the bottom and did 20 feet at a time. What he did is he took the oil saturation and integrated it over the perforated interval and compared it to production and got no correlation whatsoever. Then he took and for the four dielectric curves, which are here, you can see separation up in here, he took and he integrated the difference between the shallowest and the deep over the 20-foot interval, and called that the mobility factor. For each of these wells he took the perforated interval and did that, and then posted the first 30-day average once they started the cyclic steaming process, and then stabilized rate, the 30-day average. Then when you plot that you get a really nice trend that says as this mobility factor increases, which is as the separation increases, the oil production increases. Now, this was in a loop back mode, so it was a learning process. You see the first 30-day, the slope is this. The slope actually increases on the stabilized rate, indicating that over time this mobility factor is even more important. Then they drilled additional wells. Using this data or this knowledge that was gained they changed the completion strategy and now selected the intervals that had the best dielectric separation and again perforated 20 feet. They had some others that perforated more, but to keep everything equivalent, these are the three wells that we looked at on the previous slide. Now the scale is compressed so the slope is higher. These are the new wells that were drilled and completed based on this information, and the production in the lowest one was better or equivalent to what they had at the best well before. The best well now is double that, and these two wells are now the best producing wells in the field. They're pretty excited about it because they changed the process and it gave them the information to optimize their production. Like I said before, with diatomite before, people didn't have any way to predict productivity. They calculated oil saturation, but not productivity. This finally provided information how to do that. This is an example of steam breakthrough. In this well, another heavy oil diatomite well, where we've got the density in each run, the resistivity. Here is just the deepest 4-inch water-filled porosity curve. This kind of simulates what you had with the older technology, or if you're just looking at one depth investigation. Here it indicates that you've got good oil saturation throughout. Now this well was drilled prior to any application of steam in the field, but it was close to the lease line. The offset operator was in the process of steaming. With the data that we have available, we can see that we've got good oil saturation with a little bit of a decrease here. That's pretty much all we can see. When you add the other three curves in, you can see that a huge difference in mobility or movability that's occurred right in through here, and also have a temperature curve built in to the pattern, the tool, that shows a peak right there. This was assumed to be from the offset operator steaming. It gave them a chance to observe what was happening. But only with all four curves can you identify that something like that is happening. Other wells that correlate very well but are just back from the lease line a little bit didn't show this anomaly. There was another well that was kind of parallel to the lease line that did show it, but not as dramatically.