PowerPoint Presentation

Toggle guideBeforeA tutorial on the effect of seismic noise.(Requires lots of toggling to really see how noise interacts with signal). I start with two sets of before & after gathers to emphasize the fairly amazing success of the noise removal algorithm.after showing some results I take you on to a discussion of the noise itself, using more of the examples as prompts.

Toggle guideAfterA tutorial on the effect of seismic noise.(Requires lots of toggling to really see how noise interacts with signal). The noise has been removed by Paiges non linear logic. So toggle back and forth. I start with two sets of before & after gathers to emphasize the fairly amazing success of the noise removal algorithm.after showing some results I take you on to a discussion of the noise itself, using more of the examples as prompts.

Toggle guideBeforeI am putting these 2 before and afters up front to get you used to toggling. Ill then show some of my inverted and integrated results before continuing with a long series of further examples.

Toggle guideSome resultsAfterI am putting these 2 before and afters up front to get you used to toggling. Ill then show some of my inverted and integrated results before continuing with a long series of further examples. Whie you can see the logic got rid of a lot of multiples and refractions, the data is still noisy, so be kind in judging the results.And remember you are lookiing at the simulation of sonic logs.

Introducing my inversion/integration module Its purpose is to simulate sonic logs from seismic data. Since reflections are primarily caused by abrupt changes in velocity, this is our best hope to actually model lithology. The ability to do this is vital to long range seismic correlations.At the left is an in-line with a superimposed sonic log. Because the stratigraphy is quite regular in this study zone, the sonic log is almost generic, and no great care was taken to place it exactly (although the location is close). The purpose was to make sure my logic knew what it was doing when it indicated the presence of thick beds. As you can see, there is no problem with the well match here.The thrust of this show is noise removal Hopefully you will spend a good bit of time toggling between the input and the de-noised output. However, to prove to myself that the logic was stable for the entire volume, I ran every 20 in-lines, and two cross lines. After showing the latter, I take you back to the main toggling theme. At the end of that series I will show the in-line results.One finger on the left arrow and one on the right leads to easy toggling keep them there and it gets to be fun.

Cross line 1450, running from in-line 1222 at left, to end.This is not a normal section. Youre looking at a set of simulated sonic logs rather than at sinusoidal traces. Here, low frequencies are desired, and the approximations to lithology are of great help in long range correlations. Im playing with fault patterns, but the use of pre-stack migration has severely hurt our ability to spot fault breaks, as well as doing untold harm to the noise patterns (limiting the systems ability to detect them. The use of frequency sensitive filtering to mistakenly eliminate what they thought was ground roll did not help either. Of course we had to work with what we could get.In spite of that, these results do not seem too bad.

Toggle with next cross line.

Toggle guideBeforeBack to the meat! Reader involvement is key to understanding complexities. Toggling is the key to that end..At the lower left are a series of factoids. That form the basis of the noise removal logic.They are all either self evident, or at least easily provable. Most have been ignored by the processorsDont let them halt your toggling, but please pay attention since I think each is important.Noise removal is not 100% but I think you will agree it is a big step in the right direction. To begin let uslook at basic gatherprinciples. Each gather traceis made up of a complex overlap of primary reflections comingfrom individual reflectors, each travelingindependently . The mix does not occuruntil the receiver forces it. The final spacing between these primaries is dependent on the horizontal travel path component. Thus its likelythat the resulting mixtures from successive offsets will display different character.Continuity within the gather will depend on the separation between the involved primaries.

Toggle guideNext toggle pairAfterBack to the meat! Reader involvement is key to understanding complexities. Toggling is the key to that end..At the lower left are a series of factoids. That form the basis of the noise removal logic.They are all either self evident, or at least easily provable. Most have been ignored by the processorsDont let them halt your toggling, but please pay attention since I think each is important.Noise removal is not 100% but I think you will agree it is a big step in the right direction. To begin let uslook at basic gatherprinciples. Each gather traceis made up of a complex overlap of primary reflections comingfrom individual reflectors, each travelingindependently . The mix does not occuruntil the receiver forces it. The final spacing between these primaries is dependent on the horizontal travel path component. Thus its likelythat the resulting mixtures from successive offsets will display different character.Continuity within the gather will depend on the separation between the involved primaries.

Toggle guideBeforeWhen noise & signal are heavily mixed, nothing is clear to the eye on the gathers . At first glance it appeared there was a serious velocity error that affected the Morrow target at around one second. I built velocity correction into the system to counter that. While it seemed to work nicely on individual gathers, the overall results were hurt badly. This satisfied me we were dealing with heavy noise problems.Since the gathers have not been manipulated in any other way, the mere presence of this remarkable improvement in continuity stands on its own, both as proof of both the noise assumptions and proof of the removal logic. As we move throughthese comparison sets youll see dozens of individual events on both sides of the tuning question.After liftoff, some continue with just a broadening, and some completely break up. This illustration of seismic geometry isworth a lot of study. I try to help by pointingto some of the better examples.

Toggle guideNext toggle pairAfterWhen noise & signal are heavily mixed, nothing is clear to the eye on the gathers . At first glance it appeared there was a serious velocity error that affected the Morrow target at around one second. I built velocity correction into the system to counter that. While it seemed to work nicely on individual gathers, the overall results were hurt badly. This satisfied me we were dealing with heavy noise problems.Since the gathers have not been manipulated in any other way, the mere presence of this remarkable improvement in continuity stands on its own, both as proof of both the noise assumptions and proof of the removal logic. As we move throughthese comparison sets youll see dozens of individual events on both sides of the tuning question.After liftoff, some continue with just a broadening, and some completely break up. This illustration of seismic geometry isworth a lot of study. I try to help by pointingto some of the better examples. This is a good example and you should be toggling back and forth 4 or 5 times to get the full import.

Toggle guideBeforeWatch the arrows

Toggle guideNext toggle pairAfterWatch the arrows

Toggle guideBeforeSo, now you know what to look for, just toggle on the next pairs.Hopefully you will become convinced we are on the right track. After a while I will t discuss the noise itself, & bad things that were done in preprocessing.

Toggle guideNext toggle pairAfterSo, now you know what to look for, just toggle on the next pairs.Hopefully you will become convinced we are on the right track. After a while I will t discuss the noise itself, & bad things that were done in preprocessing.

Toggle guideBeforeConsistency is a logical test, so keep on toggling.

Toggle guideNext toggle pairAfterConsistency is a logical test, so keep on toggling.

Toggle guideBeforeNoise intro The system is seeing two types. The first are strong inter-bed multiples and the second are equally strong refractions (discussed later). The best mode for attacking both is via essentially raw data, where the curvature patterns of the two are more distinct from the reflections. Here we have to deal with velocity error, and that is not so easy.On the input slides, I ask you to pay attention to the zone starting at 800 ms.. You should be able to see a multiple pattern that begins to encroach on the the target zone around one second. As I mentioned, you have to get used to the fact that when two strong patterns mix, you only see smatterings of each. This is problem the system faces.

Toggle guideNext toggle pairAfterNoise intro The system is seeing two types. The first are strong inter-bed multiples and the second are equally strong refractions (discussed later). The best mode for attacking both is via essentially raw data, where the curvature patterns of the two are more distinct from the reflections. Here we have to deal with velocity error, and that is not so easy.On the input slides, I ask you to pay attention to the zone starting at 800 ms.. You should be able to see a multiple pattern that begins to encroach on the the target zone around one second. As I mentioned, you have to get used to the fact that when two strong patterns mix, uou only see smatterings of each. This is problem the system faces.Obviously, at least here,the system saw the multipleproblem and went a long way towards solving it.As good as this result was, we haveto face the fact that the stack alreadysees through the noise to a degree, so our final results are not always as startling as we might hope.

Toggle guideBeforeWhen you are looking at a mixture, you rarely see the full noise curvature. (the system sees it better of course).

Toggle guideNext toggle pairAfterWhen you are looking at a mixture, you rarely see the full noise curvature. (the system sees it better of course).

Toggle guideBefore

Toggle guideNext toggle pairAfterToggle guideBefore

Worth a good look.Toggle guideNext toggle pairAfter

Worth a good look.Toggle guideBefore

When you leave, I would hopeyou will at least be convinced I have proven the noise is here, and that it is clobbering our target. Whether the results are good enough to warrant more work is still the question.Toggle guideNext toggle pairAfter

When you leave, I would hopeyou will at least be convinced I have proven the noise is here, and that it is clobbering our target. Whether the results are good enough to warrant more work is still the question.Toggle guideBefore

This could easily have beenanalyzed as velocity error. However, when you see what is left after carefully detecting andlifting off the multiples, the point should be made.Toggle guideNext toggle pairAfter

This could easily have beenanalyzed as velocity error. However, when you see what is left after carefully detecting andlifting off the multiples, the point should be made.Toggle guideBefore

Non-linear coding gives us the ability to direct the scanning logic to places it can detect noise patterns.Toggle guideNext toggle pairAfter

Non-linear coding gives us the ability to direct the scanning logic to places it can detect noise patterns.Toggle guideBefore

Frequency sensitivefiltering is destructive in itsnature. It discriminates againstslopes it does not like. It is farbetter to predict noise patterns thengently lift them off, bringing out theenergy which lies below. The successof this noise removal algorithm dependson this virtue. Unfortunately this data hadbeen subjected to traditional band passfiltering before stack.Keep toggling!Toggle guideNext toggle pairAfter

Frequency sensitivefiltering is destructive in itsnature. It discriminates againstslopes it does not like. It is farbetter to predict noise patterns thengently lift them off, bringing out theenergy which lies below. The successof this noise removal algorithm dependson this virtue. Unfortunately this data hadbeen subjected to traditional band passfiltering before stack.Keep toggling!Toggle guideBefore

Once more, note the encroachment of the strong multiples on the target. Difficulty here was previously blamed on sudden polarity reversal (as explained by the AVO advocates). I do not say such phenomena never occur, but that explanation does not apply here. Toggle guideNext toggle pairAfter

Once more, note the encroachment of the strong multiples on the target. Difficulty here was previously blamed on sudden polarity reversal (as explained by the AVO advocates). I do not say such phenomena never occur, but that explanation does not apply here. Toggle guideBefore

While you will see herethat the system lifted off agood bit of the noise, it is notperfect, and the final outputstill leaves a lot to be desired. Ido believe we could do a betterjob working with raw data.Keep toggling!Toggle guideNext toggle pairAfter

While you will see herethat the system lifted off agood bit of the noise, it is notperfect, and the final outputstill leaves a lot to be desired. Ido believe we could do a betterjob working with raw data.Keep toggling!Toggle guideBefore

Watch what happens to this noise.Toggle guideNext toggle pairAfter

Watch what happens to this noise.The resolution nowshows the blurring was caused by overlap. Not perfect, but certainly better.Toggle guideBefore

Toggle guideNext toggle pairAfter

Toggle guideBefore

Last before some preliminary results (but worth some serious toggling).Shown on the next seriesare the straight stacks of every 20 de-noised in-lines coupled with their inverted andintegrated partners. Toggle guideAfter

Last before some preliminary results (but worth some serious toggling).Shown on the next seriesare the straight stacks of every 20 de-noised in-lines coupled with their inverted andintegrated partners.

Repeating the well log match to explain the reasons behind integration.The normal seismic section is made up of a series of primary reflections, from both the top and the bottom of every lithologic unit. The polarities of each pair are opposite, of course. When the two overlap, what we see on the section is the composite, and the final polarity and signal strength depends on the phase relationship of the two. This makes stratigraphic interpretation difficult.The ADAPS inversion effectively solves for the individual interfaces. Integration of these spikes ideally represents lithology (in a sonic log sense). Of course the presence of serious seismic noise effects the quality of this integration, and we have to apply low frequency corrections to keep the results from going wild. After the fact well matches tell us whether we have been successful, and this is why the comparison you see here was important.In a study of a known reservoir, stratigraphic knowledge is crucial. The fact that much of the hydrocarbon content has been extracted affects the standout of the target. Since integration makes amplitudes meaningful, this in itself is of interest.When my system detects an interface, it puts the spike at the onset of the waveform. This means our timings will be less than you see on a normal section, but they are more accurate.13 in-line comparisons follow.

The section on the left is a stack of the de-noised gathers. The one on the right is the inverted and integrated version.

The pre-processors seem to have done everything they could to make our task more difficult. As we move through this series I will point out a few of these bad processing steps, I hasten to add that what they did was pretty much industry standard.

I start with the use of pre-stack migration. Obviously there are no strong dips here to warrant such logic. The fact is that the heavy mixing inherent in the process improves the appearance. Howeve it also muddles fault breaks and wrecks havoc with noise.

Continuing the pre-stack migration objection If you study these in-lines carefully, you should see indications of faulting. I point out one or them here. Muddling by the mixing action completely obliterates much of these clues. On the integrated side, breaks in character are often our best clues., In any case this is one of the areas calling for intense visual study.

From the noise removal point of view, the most serious damage done by the pre-stack migration was the garbage it produced as a result of not being able to handle multiples and refractions in its data re-arrangement.

The noise removal logic uses pattern recognition to search out events having non-reflection lineups. Because we were not able to get raw data with no NMO correction, we had to work with velocity error, which is not so good when looking for refractions.

Another fault hint again the pre-stack migtation has pulled data across what should be breaks.

Getting a little raunchy again the noise removal is pretty good but far from perfect.

We begin to see increase amplitude on what I have tentitavely identified as the Morrow. Remember that integration has made the amplitudes more meaningful, so this could be of interest.

Again, even though there is still a lot of noise, the detail could be important.

Excuse me!

Again the amplitude factor.

This is the Of course we might pay attention to the rest of the section, but end.Click on green to repeatOr on red for router