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Non-linear deconvolution and Sonic log simulation. An effective alternative to overly ambitious inversions that require well log input and near perfect data quality. An exploration and reservoir extension tool for complex stratigraphy. ADAPS
Today’s dictionary definition of seismic inversion includes computing density and porosity. My non-linear software does not try for those attributes. Rather it aims at establishing the seismic equivalence of the sonic log. My system employs a combination of advanced, non-linear deconvolution and later integration. Since the term inversion seemed to fit, I naively used it. Unfortunately, as I explain below, this tied my work to programs I do not agree with. The first step in any inversion is deconvolution – The seismic trace can be thought of as the convolution of a downward traveling wave with a set of subsurface reflection coefficients. As far as the reflection process is concerned, these coefficients represent changes in velocity. Deconvolution is the attempt to solve for the actual coefficients, and this requires an accurate estimate of the shape of the down wave. Most early deconvolutions worked with frequency spectra to accomplish this compression, and were only able to shorten the effective wavelet. The Newer frequency domain inversions use well log information to better determine the wavelet shape. My ADAPS approach does not want such help, although subsequent well log matches are an important logical proof of its effectiveness. Proof of my non-linear approach via great well log matches will be seen later in this presentation. We can argue over what attributes determine the velocities to begin with. However we have to agree that any information we can obtain has to come from those velocities. AVO theorists state there are differences in velocity that are associated with the angle of incidence.(between the wave and the bed) and that by combining information from different spread groupings they can compute the more exotic attributes. And this is where the mathematics hit the common sense barrier - For discussion, let’s assume we trying to measure the attributes in a sand reservoir. Even though I disagree with their methods, let’s assume their reflection coefficients have been calculated correctly. Finally, although I would like to see gather sets that cleanly prove the AVO concept, let’s accept their premise, just for the sake of this argument. Now, with all that let’s look at the energy picture with a rough diagram that tries to show the potential amplitude effect from each contributing factor. However this is just background – next I talk about how ADAPS does such a good job in the time domain.
The ADAPS pattern recognition logic is a unique approach to wavelet shape determination. On a per trace basis, It computes reflection coefficient values (spikes) from wavelet guess / trace data correlations. The driving logic minimizes the un-explained energy contained in the set of input traces by.improving the wavelet guess at the end of each major pass. The logic consists of 3 layers of iteration. The first (open ended) level loops through consecutive wavelet guess passes. It starts with an initial wavelet guess that is computed using autocorrelation – like procedures. The 2 nd layered level loops through the selected set of stacked traces, and the 3 rd through the per/trace optimization..Here, at the third level, is where the coefficients are calculated (the same waveform being used for all). The system subtracts the pertinent energy (associated with each spike) from the current working trace, summing to a total. If, at the end of the major loop no improvement has been made, the system exits this phase of the operation. If improvement has been made, a new wavelet (see below) is computed, and the original, untouched trace is loaded back into the work area. This “return to the original data” keeps the system entirely honest about what it is doing. To compute the new wavelet for the next major pass, the system moves through the previous spike guesses, adding data from their effective spans to a summation vector.It then formalizes the new wavelet from this vector. Frequency domain deconvolutions have (at best) only been able to shorten the wave form, whereas ADAPS computes the spikes to an amazing degree of accuracy. In essence, I say the ADAPS deconvolution, all by itself, is an improvement on the inversions of the established competition. The need for integration - However, direct displays of our results have not excited the industry, even though they are most accurate. Making out actual lithology by following a host of reflecting interfaces requires intense visual interpretation. The same would be true in well logging, if we just displayed the velocity changes. The answer has been solved there by the creation of the sonic log (an integration of these velocity breaks). Because ADAPS has computed down to the actual spike, it is possible to simulate the lithologic picture by integrating our deconvolution results. Great well matches – on the next slide series you will see a collection of remarkable matches between our integrated output and well matches. Remember that none of this well data was used in our processing. We go on from there with a pitch for direct reservoir detection. In almost every case we give you side by side examples where you can toggle back and forth to see the effect of the integrations. We go from there to a subset of a show on direct reservoir detection. The important point to keep in mind when we get there is how seeing the bright spots properly aligned with bed lithology is vital to stratigraphic interpretation. Again, please toggle when asked.
Proving ADAPS via honest results. No well data is input! The well images you see here were super-imposed after the runs were made. The ability to match well results is the best test for any tool that is pretending to improve resolution. I present these great examples as my answer to those seeking mathematical proof. To appreciate what has been accomplished, one must compare the “before and after”. In these examples I match each final result with the best the owner had been able to do using state of the art processing.. Dominant events that were probably mapped completely change character as a result of the powerful detuning. This is illustrated by the shale play to the right where significant changes are verified by excellent well matches. This is the essence of the ADAPS argument. The best possible answer under the conditions that exist is the goal of all optimization software. In the case of ADAPS we set out to explain the energy on the individual traces using all our seismic knowhow. I am the first to say that these matches are not always perfect. However most make me smile. The ADAPS de-tuning is a two part thing. First, it does a superb job of simulating the reflection coefficients (spikes to us). However, once done, these interfaces are often hard to map, since even when the beds extend for miles, the individual spikes may come and go. ADAPS goes one step farther by integrating the spikes to simulate actual lithology. please note that on all examples there are more events showing on the input than on our output. In other words the system has completely re-arranged the input energy bands according to the true lithology. Take time to examine the improvement, lobe by lobe. A solution looking for the right problems – I have yet to see the prospect where the ADAPS results weren’t an improvement. Of course when the structure is boring the differences might not seem worth the effort. The really exciting cases are where this ultimate de-tuning brings out structural details and reservoir possibilities that otherwise might be missed. One of these is discussed two slides later. The B&A at the right comes from a different shale play. I was happy with the way the system matched the log below the arrow (thickness wise). However the upper section provides great examples of what I said about the re-assembly of the input lobes, reducing the number and greatly improving the well match. pay close attention to the circled zone. One should not have to go further for proof on the the “simulation of a sonic log” (that I tout constantly) is important. Each of the projects I have worked on has posed unique challenges. The non- linear ADAPS approach allows me to get my arms around noise problems, generally on a pre-stack basis. You are looking at seismic simulations of sonic logs, the ideal for stratigraphic interpretation.
Shale play requires even greater resolution because of the need to monitor minor fractures. Even though the improvement in the lower section is a great example of the value of integration, the detail below the arrow pointers leaves something to be desired. From what we have seen it is likely that coherent noise removal would make a big difference. The problem is getting the raw data to prove it. Back to simulated sonic logs!
What I want to show in these next two slides is how ADAPS uncovered a major lensing phenomenon. While there are traces of it on this input, you will see the major difference de-tuning can make in interpretation when you toggle to the next slide. But before you go forgive another lecture on well matches. Too often interpreters look right past the fine detail, just noticing when major things more or less line up, regardless of polarity. The reason this is so important is that these matches supply proof of ADAPS inversion and sonic log integration. On this input match, most polarities are just randomly shifted, while some are completely flipped. The rightward lobes should match with blue events of course. You will see remarkable agreement on the finished product. Please take the time to study this by toggling. This is the lensing area mentioned. It is around the target, so the phenomenon is most important to reservoir evaluation. When I first began the study I was convinced there was faulting. Now we will see that ADAPS de-tuning straightened out the complex overlap. Toggle please T The best the client could do – The unadulterated input stack.
And back To sell the merits of de-tuning, one has to find an example where it makes a profound stratigraphic difference. This is one of those finds. While I want you to study the fairly remarkable well match, first pay attention to the stratigraphic pinchout that is now evident below. A personal vignette – Back in the 50’s, when I was an interpreter for Mobil, I had an offset being drilled on my say so. We were shooting some work to check my interpretation and the paper results were drying in a locked partition. To see them I scaled the wall. Today, I wonder how many experience the excitement that still drives me in my efforts. The beauty of this well match set me off. ADAPS inverted & integrated output.
Another case where ADAPS made lobes point in the right direction. But also notice the enhanced strike slip fault evidence. BeforeAfter
Please note the minute well match details, including the polarity flip-flops.
A “post stack” example where parameters were tailored to Vibroseis input. Here I show the results first. Again note the remarkable well match. When you get used to toggling, you will wonder why no one else asked you to do it. Were they not proud of their results? Please toggle with straight stack input.
The question is: How could anyone have been satisfied with this match?
Rocky mountain (post stack) stacked input, line Y Please toggle w results
Rocky mountain (post stack) inversion and integration, line Y Please toggle w input
Another final with good well match -
And still another – (we could go on).
ADAPS direct reservoir searches- A sub-show that develops the no well input theme, centered on the prime example at the far right. ADAPS inverted and integrated final output Marine data from south Louisiana. It had very severe point source refraction problems (that are detailed in the next topic section) and I had to apply very deep mutes to get these results. As far as I know, interpreters from this company never saw these results, which is a problem when you work through people who only think in terms of time slices and other over-automated devices. I did get permission I think.
1 Three wells, three different reservoirs? We identify the main basal sand on an obvious unconformity with a series of stars. The stars identify what I consider a basal sand, sitting on one of several unconformities (angular disconformities that is). You might note this is a remarkable well match!
A C C A C C B B In-line XXX1In-line XXX2 We say the reservoir (pointed to by A (on in-line XXX1) is not the same as the “on-lap” sand tapped by well 1 at B is (on XXX2). We believe it is a basal sand sitting on the major unconformity identified as C on both inserts. We do not know if prospect A was previously identified. What we do know is that we would have pointed out prospect B as the second most probable. We could not have made these stratigraphic statements from the original section. Believe us or not, this is the argument calling for your attention. Well 1 Both pictures show onlaps to major unconformity (C). We will show later that our ADAPS pre-stack and inversion + integration processing greatly clarified this structure, but that is not our current point. The question is whether the “bright spots” you see would have predicted the results in the several wells which had been previously drilled. The fact that no well input was used in our processing makes this a vital exercise. Our proof lies in the excellent well matches we‘re showing. Remember that the straight stack contains primary events from all bed interfaces. Integration gets rid of at least half of the lobes, completely rearranging the total energy. The result simulates a sonic log picture of the lithology. We proudly call our output a series of simulated sonic logs for a reason! If there are reservoirs we promise you will see them highlighted. That is the name of the first game in this study. The well matches show this to be true. You will notice many other more promising leads as you look through these slides.. But before you leave this slide, spend a little time looking at how great this well match really is.
Here is well #3. It appears to be tapping below our basal sand (again identified by stars). It is interesting to note that it (the basal sand) has petered out here. This is a problem we should expect in “on-lapping” deposition. Again this is a great match.
well 7 Not knowing anything about the well results, we would say this one looks like a loser. In any case, the bottom sand does not correlate with any of the others. It should be fairly obvious by now that we think we see better places to drill.
Out of the box thinking does not mean unscientific, but more explanation is required. Every ADAPS in line and cross line starts from scratch. Connecting them in space with no fudging is strong proof of the entire process. The next 4 slides show such independent connections. They also provide a great theatre on visual interpretation. When I say we start from scratch I mean we treated each side of each connection as if it were the only one we had to work with. So pay attention to the stratigraphic detail and reservoir hints we think the major oil company missed.
In-line xx1 connected to cross-line 2500
In-line XX2 connected to cross-line 2700
In-line XX5 and cross-line 2900
In-line XX6 connected to cross-line 2900 We rest our case.
Allow them time to load! A PowerPoint compendium of seismic topics Basic reflection theory the experts seemed to have missed. Inversion and integration – the all important sonic log simulation. Coherent noise background – an explanation of types. Removal of coherent noise on Gulf Coast – a breakthrough. Vibroseis de-noising – another (but similar) breakthrough. Strike slip faulting – salt dome association – new thinking. North Sea strike slip interpretation – the importance of resolution. About Paige - MS in geology,spent 7 years in Venezuela for Mobil,& then Phillips Maracaibo interpretation found Phillips’ major field there. Back to states, joined Phillips computing, became project manager for exploration. Hired by Western Geo. To start digital operations in Shreveport. Wrote first predictive deconvolution program that put Western on the map in digital processing (and formed the non-linear basis for later ADAPS software), After brief sojourn in commercial processing (where he wrote a table driven programming system), joined Dresser Olympic as both manager of processing and of research. Went on his own to start non-linear development. Consulting package consists of Paige’s personal time, his open-ended software and use of his processing hardware. Unless full segy detail is requested (segy output), the product is a series of PowerPoint studies. He can be reached at