© Distribution of this video is restricted by its owner
00:01 | this conference will now be recorded. , Thank you very much. |
|
|
00:08 | 3rd time for the record in composite modeling, mixing constituents, we could |
|
|
00:15 | wide bounds which are the void and pounds. They're the widest bounds tighter |
|
|
00:20 | are machines, Brickman and with specific , we can uh make an exact |
|
|
00:28 | um and when the end member properties close to each other, we're safer |
|
|
00:35 | a linear relationship. The wider those properties are the more different they are |
|
|
00:41 | each other. The more potential Garrity. Mhm. All right |
|
|
00:50 | what? What is a constituent? here we have a highly idealized |
|
|
00:58 | We have the matrix or the background material, which is the white. |
|
|
01:07 | then have added solid inclusions which are black and this was a scanned |
|
|
01:15 | So some of these are not so . So you can imagine some uh |
|
|
01:19 | ferocity inside the that solid material. then we've also added pore spaces which |
|
|
01:26 | are these circular objects And in this then the Yeah, dominant mineral is |
|
|
01:37 | background matrix. The secondary mineral is an inclusion that is embedded within that |
|
|
01:46 | . And the ferocity is also treated as the constituent either in a dry |
|
|
01:55 | , it's treated as air. And using the elastic properties of air and |
|
|
02:01 | of air or in a liquid filled or fluid filled rock. Uh could |
|
|
02:08 | a gas water mixture, whatever we the effective properties of the fluid in |
|
|
02:14 | course face. So the poor whatever they are, are explicitly treated |
|
|
02:22 | constituents. So this particular Rappe rock be what we would call a turn |
|
|
02:30 | mixture because we would have three The white matrix solid material, the |
|
|
02:38 | secondary mineral And the fluid in the space. So three constituents. So |
|
|
02:46 | one way to look at the And when we extend the uh wildly |
|
|
02:53 | average equation, for example, to minerals, that's what we did. |
|
|
02:58 | added up the travel time in each , the travel time and the fluid |
|
|
03:04 | the travel time in each of the . However, there is another way |
|
|
03:08 | look at the world uh instead of Iraq as having or the Iraq is |
|
|
03:17 | pour fluids as a explicit constituent, can treat the pore space and whatever |
|
|
03:27 | filling the pore space, we can it implicitly where instead of mixing zero |
|
|
03:34 | minerals and the pore filling material, can mix porous rocks. So we |
|
|
03:44 | the white rock which is porous. it has different properties now than the |
|
|
03:51 | mineral, but it's the properties of porous, a pure mono mineral lick |
|
|
03:58 | and we can mix that with inclusions porous material. So it's a different |
|
|
04:04 | of the world uh to the, know, obviously this may be very |
|
|
04:11 | in a certain situation where you have of one mineral embedded in another. |
|
|
04:17 | then again, if you have uh an inter penetrating pore space. It |
|
|
04:23 | be reasonable to think about things this where both solids, BB it white |
|
|
04:29 | black are in contact with and surround space. So this is what we |
|
|
04:37 | call a ferocity implicit model. as a first order approximation we could |
|
|
04:45 | the ferocity in both minerals is the . So the ferocity and the white |
|
|
04:51 | equals the ferocity and the black that's the first order approximation, but |
|
|
04:57 | not required to do that. You uh if you have a priori knowledge |
|
|
05:03 | some reason you think one mineral we may have more ferocity associated with |
|
|
05:09 | you could have different Torosidis. But the first order approximation, without any |
|
|
05:14 | information, we could just assume the is the same. So examples of |
|
|
05:26 | , explicit equations, we talked about time average equation where the total travel |
|
|
05:32 | is the sum of the travel times the individual constituent, individual minerals, |
|
|
05:39 | the volume fraction of that mineral plus coefficient times ferocity. And that |
|
|
05:47 | in the case of the wildly time equation is the fluid transit time. |
|
|
05:52 | in fact I could have crossed the . K. Ferocity out over here |
|
|
05:57 | I could just have treated poor fluid a constituent. Use the transit time |
|
|
06:03 | the poor fluid and the volume fraction the poor fluid is the ferocity. |
|
|
06:07 | the widely equation is very explicitly treating or the material in the ferocity treating |
|
|
06:16 | as a constituent. Uh We also the han equations uh coming out of |
|
|
06:24 | , um and you can do the thing by averaging velocity right now, |
|
|
06:32 | we saw a lot of different velocity relationships and anyone circumstance uh, widely |
|
|
06:39 | average equation form may work better or average may work better. I haven't |
|
|
06:46 | a compelling reason why one should use over the other. The widely time |
|
|
06:52 | equation equation was convenient when people were with sonic logs because sonic logs were |
|
|
06:58 | and units of transit time. So became a more popular use, But |
|
|
07:04 | not sure that there's ever been any of study deciding statistically which one tends |
|
|
07:10 | match ferocity data better. You could it either way. And in a |
|
|
07:16 | circumstance, one for may work better the other forms from. By the |
|
|
07:24 | , this forum is also in a lick. Rock was first introduced by |
|
|
07:31 | . So he said, the velocity a monumental alec rock is equal to |
|
|
07:36 | constant times ferocity plus an intercept. we'll refer to that as a hicks |
|
|
07:44 | and it's convenient to use. And we want to uh make the porosity |
|
|
07:53 | , if we want to remove ferocity these equations, remember these are the |
|
|
07:58 | fans of times or the mineral We can remove ferocity from the equations |
|
|
08:06 | just take uh the velocity average or average of porous rocks. So instead |
|
|
08:15 | course calcite and dolomite, we have limestone and dolomite rock. And so |
|
|
08:23 | for example, we could do a average. We could also do a |
|
|
08:26 | average. Um Later on we'll see uh this uh tends to give you |
|
|
08:35 | higher velocity for the composite uh than means of averaging. Remember we talked |
|
|
08:42 | reciprocal averages like with the Royce And later on we'll show that's doing |
|
|
08:49 | in between a linear average in a average of velocity seems to work pretty |
|
|
08:56 | . But anyway, in its simplest , you could do things this |
|
|
09:00 | So the X. I. And V. I. R. Now |
|
|
09:04 | the volume fraction and velocity of the mineral, the volume fraction of the |
|
|
09:13 | rock over the poorest rock constituents. so for each constituent then we could |
|
|
09:21 | a velocity ferocity relationship. We could anyone we want, it could be |
|
|
09:26 | dreamer and Gardner equation. It could the critical ferocity model. Uh It |
|
|
09:31 | be anything, it could be going Gardner's relations if these are unconsolidated |
|
|
09:37 | So uh it's immaterial. Uh you a velocity porosity relationship then, which |
|
|
09:45 | be with ology dependent which you now to here and then average those |
|
|
09:52 | Like I said, it doesn't have be a linear average. It could |
|
|
09:56 | something else that gives you a lower . Okay, so now which of |
|
|
10:06 | forms matches or measurements better porosity explicit prosperity implicit. And so here this |
|
|
10:16 | a database of a few 100 rock measurements. And it's uh velocity vp |
|
|
10:25 | top V. S on the bottom comparing for in Mexico pathologies and over |
|
|
10:33 | ferocity ease comparing predicted in actual And you see what happens is if |
|
|
10:40 | use a ferocity explicit model, notice limitation here. And the limitation is |
|
|
10:48 | the dependence on ferocity becomes mythology Right? The mythology goes into this |
|
|
10:57 | and it doesn't vary. The Prasit dependence doesn't vary with the mythology. |
|
|
11:05 | you can imagine if you had different poor shapes associated with different mythologies that |
|
|
11:11 | might you might have a different velocity relationship in different mythologies which we can |
|
|
11:21 | here. And so you can see that's exactly what happens if we compare |
|
|
11:28 | in actual velocities I measured in the . You see, it's it's |
|
|
11:34 | Uh it's not on the diagonal. And so there are biases. The |
|
|
11:41 | velocity rocks uh tend to, the tends to be too high, as |
|
|
11:48 | the do the high velocity rocks and intermediate velocities tend to be to |
|
|
11:55 | And same thing for V. And G. S. In |
|
|
12:00 | the standard deviation here is this is feet per second. So for bp |
|
|
12:06 | ft per second For v. It's at first glance is better 800 |
|
|
12:13 | . But remember the shear wave velocities lower. So actually this is a |
|
|
12:18 | percent error. On the other if we use a process, the |
|
|
12:24 | model, we could remove these biases so now we're getting a nice linear |
|
|
12:32 | with, with a much better standard , much smaller prediction error. Now |
|
|
12:43 | could take the same approach and we uh try to predict shear wave velocity |
|
|
12:51 | p wave velocity. For example, it's suppose I'm trying to predict shear |
|
|
12:59 | velocity of the composite. I could in here the predicted shear wave velocity |
|
|
13:05 | any pure mineral. So instead of a porosity velocity relationship here, I |
|
|
13:12 | have a V. P. S relationship. So for example I |
|
|
13:16 | use the uh what tend to be nowadays the Greenberg and Castagna Relations which |
|
|
13:23 | gave you before uh you know, unit on the PBS rations. |
|
|
13:27 | So here beta share with velocity help P wave velocity and from different mythologies |
|
|
13:37 | have different coefficients. Uh huh. kind of funny that the equations we |
|
|
13:44 | were not our preferred equations. They equations which we were allowed to publish |
|
|
13:51 | a subset of the data we which was public domain data. So |
|
|
13:56 | were never allowed to publish the best that we had. But anyway, |
|
|
14:02 | is what's made it into the literature this is what most people use, |
|
|
14:07 | guess the the actual trends are still at BP if anybody because they acquired |
|
|
14:14 | company I was working for. I doubt if anybody is using those equations |
|
|
14:21 | , but people tend to be satisfied these equations. So what you do |
|
|
14:26 | you stick these equations in here and you can predict the velocity, but |
|
|
14:34 | than a simple average like this, in comparing to a lot of |
|
|
14:40 | uh we found that using a different was better and I'll show you that |
|
|
14:45 | a minute. Um so the idea is that xu wei velocity laws are |
|
|
14:53 | not available in the wells are old people that want to spend the money |
|
|
14:58 | acquire a sheer way blog. And uh if you have a few red |
|
|
15:04 | or you have a pseudo atomic, could be computed from the resistive Itty |
|
|
15:10 | neutron log or whatever, wherever you your p wave velocity log, if |
|
|
15:15 | have an idea of the lethality, then you could use V PBS trends |
|
|
15:21 | VP density trends and you could uh shear wave velocities. So in a |
|
|
15:29 | case you would bring conventional logs he would do a volumetric log |
|
|
15:34 | schedule mythology log, you have a p wave velocity log, you use |
|
|
15:41 | trend curves and you could predict a philosophy. Remember we never have pure |
|
|
15:48 | , pure limestone, pure dull but we can mix uh we make |
|
|
15:55 | predictions of the velocities, it eat the pure mythology. So from |
|
|
16:00 | we predict Bs in the pure mythologies then we do some kind of averaging |
|
|
16:08 | millet ology. So, again, a empirical from cause it medium |
|
|
16:16 | Okay, and this is the equation used, I mean in the simplest |
|
|
16:21 | you could ignore all ideologies and this better than pulling a number out of |
|
|
16:26 | air. You could use the mud line for example. But of course |
|
|
16:31 | going to be wrong if if you've clean sands or you've got lime stones |
|
|
16:36 | you have hydro carbons. Right? this might get you somewhere in the |
|
|
16:40 | but it won't be very precise and deviations from the mud rock line will |
|
|
16:46 | you very significant differences in seismic So we want to get more precise |
|
|
16:52 | that. And so here, uh equation looks complicated, but it's actually |
|
|
16:58 | simple. The X's are the volume , the alphas or the T wave |
|
|
17:05 | . And and so uh the alphas are the coefficients like the Greenberg and |
|
|
17:11 | coefficients. All right, so what do is for each mythology, we |
|
|
17:20 | the shear wave velocity for that Uh based on some of the |
|
|
17:28 | Uh well, basically using these trends . So this winds up being the |
|
|
17:35 | shear wave velocity And we do that we repeat that here except there's a |
|
|
17:42 | . So what we're doing here is similar to the hill average. We're |
|
|
17:45 | of cutting it and began the velocity in half. This would be the |
|
|
17:52 | kind of like avoid average and this be the softest kind of like a |
|
|
17:57 | average. It's not in module it's velocities but it's the same idea. |
|
|
18:03 | The answer is probably somewhere in between two. It could be on |
|
|
18:07 | it could be the other could be in between we don't know where. |
|
|
18:11 | we just average the two and that us again in the ballpark. And |
|
|
18:17 | that so we take the predicted Bs the D. P. V. |
|
|
18:21 | trend. Uh And we take a average a reciprocal average. We can |
|
|
18:27 | average those two, I should Yeah, linear volume weighted average, |
|
|
18:33 | reciprocal volume weighted average and half the . And that gives us our predicted |
|
|
18:40 | way philosophy. So if we don't hyper carbons, this is very simple |
|
|
18:45 | remember these V. PBS trends are bronze saturated rocks. Now this approach |
|
|
18:52 | work if you have hydrocarbons so you to do one better. And um |
|
|
18:58 | anyway, I'm brian saturated rocks. tested this on laboratory data. So |
|
|
19:04 | is predicted Bs versus observe. Es good prediction. Uh Here this is |
|
|
19:13 | a publication uh The original Greenberg and publication. This was all the laboratory |
|
|
19:20 | we had and so that was how did there and these were on what |
|
|
19:26 | saturated velocities. We also did the thing on airfield rocks and the prediction |
|
|
19:36 | was pretty good. Uh We have lot of success applying this to |
|
|
19:42 | And um actually the predictions sometimes seem defy belief. Right? So here |
|
|
19:51 | have a with ology log here, have the sauna cloth and vegetable sonic |
|
|
19:58 | and here we're comparing the predicted to measured shear wave lock. And you |
|
|
20:04 | see that they're right on top of other. That's why this algorithm has |
|
|
20:09 | so popular. Uh There are discrepancies where we have into betting and some |
|
|
20:16 | the thin layers and it's kind of . But at the time internally within |
|
|
20:24 | company we were working, a very friend of mine had inherited the sonic |
|
|
20:32 | research group. I had moved over another department. He had taken it |
|
|
20:38 | . We're still very close friends to day but it was a little bit |
|
|
20:42 | a competition because he was measuring the and I was coming along saying I |
|
|
20:47 | predict the logs and so uh it until we sat down and we decided |
|
|
20:53 | it's worthwhile to do both. That prediction is a good to see uh |
|
|
20:59 | the measurements and the measurements are necessary perfectly calibrate your prediction. And when |
|
|
21:06 | have discrepancies, that's an opportunity to something. Actually, it turned out |
|
|
21:12 | be very useful to be able to this. So a few things uh |
|
|
21:19 | out of this particular example and that when we used the gamma ray log |
|
|
21:24 | predict mythology, We have a lousy . Uh and our conclusion was that |
|
|
21:31 | mythology prediction was bad with the You have variable radioactive content in |
|
|
21:40 | You know, it's not just potassium story. Um it's uranium uh and |
|
|
21:47 | in your sands, you have radioactive . If it's an oil reservoir, |
|
|
21:52 | could have uranium precipitated in the pore . Uh If you have radioactive |
|
|
21:59 | uh if you have organic material which is associated with uranium, so the |
|
|
22:07 | ray log is very imperfect for estimating . Uh This was south texas. |
|
|
22:17 | decided that the neutron density log would a good way to estimate with ology |
|
|
22:22 | that's what was done here. And if we didn't iterate on the mythology |
|
|
22:27 | force to match, we just did with ology in a different way. |
|
|
22:31 | use the gamma ray log and we're to get a very perfect prediction. |
|
|
22:37 | calibration, This is using the global curves. That was what was most |
|
|
22:43 | about it. In fact, I so impressed with these results, then |
|
|
22:48 | was wondering why in these thin the prediction was actually going to lower |
|
|
22:55 | . Whereas the measurements seem to be off in those very thin layers and |
|
|
23:02 | are the shear wave logs, you have a means of estimating the quality |
|
|
23:10 | share wave laws because the velocities are very much the same way we measure |
|
|
23:16 | velocities. There's a semblance algorithm that at the similarity of wave forms and |
|
|
23:23 | the time lag where you have maximum . But the semblance value itself is |
|
|
23:30 | indication of how similar the way forms and the higher the semblance, the |
|
|
23:36 | your measurement is. So for this we went in and this is just |
|
|
23:40 | piece of the log. The log much longer, We had quite a |
|
|
23:45 | of statistics here, so we were to been the discrepancy between the |
|
|
23:53 | Right, So we look at the called error, let's just call it |
|
|
23:57 | discrepancy between the measurement and the It doesn't mean that the predictions wrong |
|
|
24:03 | that amount because there is experimental error the measurement. So, uh, |
|
|
24:09 | know, there's a discrepancy and if and if we plot below the standard |
|
|
24:18 | relative to the semblance. So we've all the values with high semblance, |
|
|
24:24 | know, so these are bins of and we measure the standard deviation of |
|
|
24:31 | discrepancy. And what we find a of one would mean you have identical |
|
|
24:37 | forms with identical amplitude, So you'll hit one. Uh but what you |
|
|
24:44 | is a definite trend as the semblance getting better, the discrepancy is getting |
|
|
24:51 | and you can then predict, you , where you're perfect, you |
|
|
24:57 | when you have perfect semblance what the would be. So that would be |
|
|
25:03 | best indication of your prediction error, what we also found was when you |
|
|
25:09 | very bad templars, you had really discrepancy and so I would argue. |
|
|
25:17 | where is this? This intercept would the prediction errors that were on the |
|
|
25:22 | of You know, 12 or something that, where the semblance is very |
|
|
25:27 | and you have very poor confidence in velocity measurement. We had a semblance |
|
|
25:33 | there around 30. So the point the discrepancy here we believe is more |
|
|
25:39 | error and the discrepancy here is more error. So anyway, quite a |
|
|
25:46 | prediction. And I would argue that these thin layers if we plotted the |
|
|
25:51 | , you would see that where things clipped here is you have low semblance |
|
|
25:56 | and and that's because of interferences. have actually is the seismic refracted wave |
|
|
26:02 | propagating every time you have an interface develop reflected and transmitted refracted waves and |
|
|
26:11 | get a complex interference pattern and you start your your way for him and |
|
|
26:18 | velocity measurements not very good. Just example. These were actually paleozoic shells |
|
|
26:30 | . And quite a good prediction Uh and here was the cross plot |
|
|
26:37 | predicted uh birth is measured. These off door die paul measurements and uh |
|
|
26:49 | was back in the early 90s by were just becoming popular and uh so |
|
|
26:56 | flooding the predicted shear wave velocity against measured shear wave velocity. This came |
|
|
27:02 | the scientific publications that we have to , you know, M. |
|
|
27:06 | S. Units. Um Overall pretty high correlation coefficient .9 uh various 32 |
|
|
27:18 | for kilometer. Okay, so uh is in transit time but you can |
|
|
27:25 | that that's a pretty small error in time. And what was interesting is |
|
|
27:34 | this particular? Well we had two typos tools run two different shareware vlogs |
|
|
27:41 | so we said, how well do agree with each other? And what |
|
|
27:46 | found was that the measurements, The was only .87. Where is the |
|
|
27:54 | the court to one of the The correlation was .9. And why |
|
|
28:00 | would it be worked? Why would measurements repeat measurements be worse? And |
|
|
28:06 | we tried to correct for variable depth stretch but maybe we didn't correct it |
|
|
28:14 | . So you know, you your depth registration is not always exactly |
|
|
28:20 | but we did try our best to that. So the results are comparable |
|
|
28:30 | another example and this this is showing well how in thin layers the direct |
|
|
28:36 | tend to go bad. Right? so the line is the prediction and |
|
|
28:42 | is in there. That was A variety of applications came out of |
|
|
28:50 | are drilling engineering group was very you know, they needed to measure |
|
|
29:00 | ratio for hydraulic fracturing design and this a really interesting case. This was |
|
|
29:06 | North Sea blow out well and uh was a litigation basically with the british |
|
|
29:18 | , we wanted to drill a relief , it was a lot like what |
|
|
29:21 | with the deepwater horizon. But this in the North Sea and we had |
|
|
29:25 | convince the british government to let us a relief well. Whereas uh with |
|
|
29:31 | deepwater horizon it was the government was to be convinced as to a remedy |
|
|
29:38 | they let the fluids flow the oil for months before they allowed BP to |
|
|
29:45 | what BP needed to do to to the well in. But anyway, |
|
|
29:51 | was in the North Sea and to the government, we have to show |
|
|
29:55 | that we could predict the closure And so here we have uh the |
|
|
30:03 | poor pressure the by the way this after logging. Right? So we |
|
|
30:08 | the pore pressure, we have the drilling mud pressure which is slightly above |
|
|
30:16 | pore pressure, which is what you . Uh We have the calculated horizontal |
|
|
30:24 | and this is from the log. you need persistence ratio and you need |
|
|
30:31 | what we consider proprietary algorithms to predict closure stress. There's, there's a |
|
|
30:38 | slumber J equation that people use, not what we use. But uh |
|
|
30:44 | then these were the leak off These are the measured closure stresses and |
|
|
30:49 | can see we pretty well nailed it those slightly more permeable units there. |
|
|
30:58 | so we were able to convince the that we knew what we were |
|
|
31:03 | And we could drill the relief wells that's because we had of persons ratio |
|
|
31:09 | , which had not been measured, which we calculated using these techniques. |
|
|
31:19 | . But things get complicated when you hydro carpets. Uh We said it's |
|
|
31:27 | a simple averaging equation From VPBS trends were are for 100% brine saturated |
|
|
31:36 | But what do you do if the saturation is not one, you don't |
|
|
31:43 | the trend curve to use. So this is what we assume we |
|
|
31:48 | coming in, we have p wave , ferocity, water saturation, the |
|
|
31:54 | and fluid fractions and properties and the . And the way the algorithm that |
|
|
32:02 | is we take an initial guess at the p wave velocity would be if |
|
|
32:09 | water saturation was one that doesn't have be a very precise gas. Uh |
|
|
32:15 | uh well, iterating until we get the right answer, but uh if |
|
|
32:20 | in the ballpark things will converge Right? So, well, guess |
|
|
32:25 | the p wave velocity would have had we had 100% brine saturated |
|
|
32:30 | And in that case then if W equals one, we could use |
|
|
32:37 | B. P. B. Trends and we could use the mixing |
|
|
32:42 | and we could then predict the shear velocity what the shear wave velocity would |
|
|
32:47 | been, had the formation had a saturation of one from that and from |
|
|
32:56 | from the density, we will then be able to, and prosperity will |
|
|
33:02 | able to compute what the bulk and modules are. And those are probably |
|
|
33:08 | because our shear wave velocity is probably until we converge to the right |
|
|
33:14 | But let's let's run with them, what we get. Uh we uh |
|
|
33:21 | the dry frame properties knowing that is wrong. But then, given that |
|
|
33:30 | know we have the measured sw, think we know what the fluid properties |
|
|
33:34 | from the battle and wang equations. we could uh then uh at the |
|
|
33:40 | water saturation, we could go the way and we could predict the p |
|
|
33:45 | philosophy. And if our initial guest right, then we'll predict the p |
|
|
33:51 | velocity correctly. If the original guest wrong, then our predicted VP will |
|
|
33:59 | match the measured VP. And so go back and iterate. Or you |
|
|
34:04 | do steep newton, steepest descent or kind of optimization algorithm you want. |
|
|
34:10 | and you could uh you could repeat process until your predicted vP matches the |
|
|
34:18 | vP. Uh So in the output this process, we get two |
|
|
34:25 | we get the shear wave velocity at institute saturation. So that's the what |
|
|
34:32 | measured shear wave velocity would have been , we had the log And we |
|
|
34:37 | get the p wave velocity at award of one. Uh We could uh |
|
|
34:44 | this in the other direction. If starting with water saturation equal one we |
|
|
34:50 | then compute V. S and P at a new water saturation because |
|
|
34:55 | the drive, once we've got the module list and we could produce an |
|
|
35:01 | at any saturation we want. And so in the paper, this was |
|
|
35:10 | flow chart for the algorithm and uh was going to try to take you |
|
|
35:14 | it, but I found that this a much clearer way to present |
|
|
35:19 | But but just for uh the record is the flow chart for the algorithm |
|
|
35:27 | the Greenberg and Castagna 1992 paper. , so uh again, for historical |
|
|
35:39 | , we do this uh back, know, if you ever heard of |
|
|
35:43 | company called Arco, we were all for our co at the time we |
|
|
35:48 | this and this was an example of print out of the program at a |
|
|
35:55 | death. Right? And so you your inputs, you have your measured |
|
|
36:02 | saturation, you also have the desired statue saturation. So the water saturation |
|
|
36:09 | which you'd like to do the So now we could do not only |
|
|
36:13 | wave velocity prediction, but we could fluid substitution. And of course you |
|
|
36:18 | to know the ferocity now and then predict the velocity density and shear wave |
|
|
36:27 | at the desired new water saturation. uh we could use this for fluid |
|
|
36:37 | . And so here is an example we flew substituted in this range here |
|
|
36:43 | this interval and we put different fluids we had gas live oil, dead |
|
|
36:50 | . This was the p wave velocity . Of course the shear wave velocity |
|
|
36:54 | not going to change very much with fluid as we learned in the last |
|
|
37:02 | are one of the driving forces for this was being able to model offset |
|
|
37:11 | so we can model the HBO the variation with offset. And so uh |
|
|
37:18 | had a synthetic wave propagation program and it to actual gathers. And what |
|
|
37:25 | found was that our predictions were agreeing the data. Right? So um |
|
|
37:32 | solid curve here is the predicted a from from the synthetic gather and uh |
|
|
37:41 | data points here are from the real and the dash line is the fit |
|
|
37:48 | . I'm sorry. It's the other around the dash line is the prediction |
|
|
37:51 | it goes all the way to zero incidents. And the black line is |
|
|
37:56 | fit a statistical fit to the data presumably that scatter is noise lot of |
|
|
38:08 | . Um This was a gas reservoir the way, this was a shallow |
|
|
38:14 | reservoir. It was an amplitude increased offset. Uh This was another gas |
|
|
38:20 | above an oil reservoir and it was interesting case. Uh of course in |
|
|
38:25 | gas reservoir were predicting a very low . PBS ratio and we're matching the |
|
|
38:31 | essentially perfectly. The oil reservoir was . It was a low G. |
|
|
38:37 | . R. And in the oil were actually predicting a higher V. |
|
|
38:43 | . V. S. Ratio. And that's because it was a very |
|
|
38:46 | sand and the shells were relatively hard hard. So the shells actually were |
|
|
38:55 | the PBS ratio than the oil filled . And that produced a relatively |
|
|
39:01 | Yeah. Now as I mentioned where you have a discrepancy, uh |
|
|
39:09 | uh it's an opportunity to try to why you have the discrepancy. So |
|
|
39:15 | we have a with ology log here have our V. P. |
|
|
39:18 | S. Log which by the way sheer wave transit time divided by p |
|
|
39:22 | transit time. That is the P. B. S ratio. |
|
|
39:26 | here was the measured shear wave transit . So slow as to the right |
|
|
39:39 | If you do this, assuming the saturation of one, then you you |
|
|
39:45 | be able to find discrepancies that are with gas and what will happen, |
|
|
39:51 | you're predicted shear wave velocity will be low because you're assuming your brine |
|
|
39:56 | you're predicting hi V. P. . S ratio. So given a |
|
|
40:02 | VP, you'll predict a high P. V. S. And |
|
|
40:06 | predicted Bs will be too low and could be in the indicative of |
|
|
40:11 | especially if you have low resistive, wet. Hey it might be a |
|
|
40:15 | of recognizing that this was an interesting because we didn't have a shear wave |
|
|
40:29 | in this case. What we had the P wave sonic log. And |
|
|
40:33 | we had a very finely sampled This was before in these relatively shallow |
|
|
40:42 | , relatively low velocities. See these high transit times. We in the |
|
|
40:49 | days we didn't have reliable sherwood So, uh what we have then |
|
|
40:58 | our predicted be PBS ratio, which the solid curve. And we're comparing |
|
|
41:04 | to the VSP measured ves. So is kind of, you know, |
|
|
41:09 | think like a kind of a mud line prediction. And it agrees pretty |
|
|
41:14 | with the DSP, some problems in , but maybe we didn't have the |
|
|
41:18 | quite right. But then we had serious deviation. If we continue assuming |
|
|
41:25 | saturation, we're predicting a higher PBS ratio. A slower shear wave |
|
|
41:31 | time. And the VSP bears Right. And what we find is |
|
|
41:40 | we have to use a Mhm. gas that trade if we use the |
|
|
41:46 | saturated logged and or assume that the for gas saturated, we would predict |
|
|
41:55 | velocities here. And what you can is that the BSP is either measuring |
|
|
42:03 | close to our predicted gas that traded here and here, we're somewhere in |
|
|
42:09 | gas and brine suggesting that there's gas the section someplace. All right. |
|
|
42:16 | we have limited resolution and this was fairly large interval here. So, |
|
|
42:24 | not suggesting that we have 1000 ft pay Or more. What is |
|
|
42:29 | 21,500 ft of pay. We're not that what we're suggesting, uh, |
|
|
42:35 | gas in the system so we might in some kind of micro slippage |
|
|
42:51 | Um Also remember we if we have and S wave velocity is predicted, |
|
|
42:56 | could then calculate elastic module I and are used for engineering applications. This |
|
|
43:04 | on the north slope of Alaska. was a limestone reservoir there, the |
|
|
43:10 | formation. And we had a pretty predictions of share module lists in bulk |
|
|
43:21 | . Okay. There's a lot more can talk about and uh, I |
|
|
43:30 | think I want I want to I it's too much and so I'm going |
|
|
43:36 | cut things short today, so I'm here. Um, are there any |
|
|
43:44 | then? So yeah, I this is all your notes go there |
|
|
43:52 | a lot more slides here, but think it's it's more than I want |
|
|
43:57 | to have to worry about. even though they're in your notes, |
|
|
44:01 | won't be on the test. So at this point. Okay. Any |
|
|
44:09 | ? Yes, |
|