1. Toggle guide Before A 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.

2. Toggle guide After A 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 Paige ’ s 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.

3. Toggle guide Before I am putting these 2 before and afters up front to get you used to toggling. I ’ ll then show some of my inverted and integrated results before continuing with a long series of further examples.

4. Toggle guide Some results After I am putting these 2 before and afters up front to get you used to toggling. I ’ ll 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.

5. Introducing my inversion/integration module – It ’ s 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.

6. Cross line 1450, running from in-line 1222 at left, to end. This is not a normal section. You ’ re 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. I ’ m 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 system ’ s 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.

7. Cross line 1550, running from in-line 1222 at left, to end. This is not a normal section. You ’ re 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. I ’ m 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 system ’ s 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 last cross line. Next, before ending with a series of before and after gathers, we look at a set of in-lines.

8. Toggle guide Before Back 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 processors Don ’ t 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 us look at basic gather principles. Each gather trace is made up of a complex overlap of primary reflections coming from individual reflectors, each traveling independently. The mix does not occur until the receiver forces it. The final spacing between these primaries is dependent on the horizontal travel path component. Thus it ’ s likely that the resulting mixtures from successive offsets will display different character. Continuity within the gather will depend on the separation between the involved primaries.

9. Toggle guide Next toggle pair After Back 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 processors Don ’ t 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 us look at basic gather principles. Each gather trace is made up of a complex overlap of primary reflections coming from individual reflectors, each traveling independently. The mix does not occur until the receiver forces it. The final spacing between these primaries is dependent on the horizontal travel path component. Thus it ’ s likely that the resulting mixtures from successive offsets will display different character. Continuity within the gather will depend on the separation between the involved primaries.

10. Toggle guide Before When 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 through these comparison sets you ’ ll 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 is worth a lot of study. I try to help by pointing to some of the better examples.

11. Toggle guide Next toggle pair After When 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 through these comparison sets you ’ ll 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 is worth a lot of study. I try to help by pointing to 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.

12. Toggle guide Before Watch the arrows

13. Toggle guide Next toggle pair After Watch the arrows

14. Toggle guide Before So, 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.

15. Toggle guide Next toggle pair After So, 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.

16. Toggle guide Before Consistency is a logical test, so keep on toggling.

17. Toggle guide Next toggle pair After Consistency is a logical test, so keep on toggling.

18. Toggle guide Before Noise 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.

19. Toggle guide Next toggle pair After Noise 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 multiple problem and went a long way towards solving it. As good as this result was, we have to face the fact that the stack already sees through the noise to a degree, so our final results are not always as startling as we might hope.

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

21. Toggle guide Next toggle pair After When you are looking at a mixture, you rarely see the full noise curvature. (the system sees it better of course).

22. Toggle guide Before

23. Toggle guide Next toggle pair After

24. Toggle guide Before Worth a good look.

25. Toggle guide Next toggle pair After Worth a good look.

26. Toggle guide Before When you leave, I would hope you 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.

27. Toggle guide Next toggle pair After When you leave, I would hope you 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.

28. Toggle guide Before This could easily have been analyzed as velocity error. However, when you see what is left after carefully detecting and lifting off the multiples, the point should be made.

29. Toggle guide Next toggle pair After This could easily have been analyzed as velocity error. However, when you see what is left after carefully detecting and lifting off the multiples, the point should be made.

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

31. Toggle guide Next toggle pair After Non-linear coding gives us the ability to direct the scanning logic to places it can detect noise patterns.

32. Toggle guide Before Frequency sensitive filtering is destructive in its nature. It discriminates against “ slopes ” it does not like. It is far better to predict noise patterns then gently lift them off, bringing out the energy which lies below. The success of this noise removal algorithm depends on this virtue. Unfortunately this data had been subjected to traditional band pass filtering before stack. Keep toggling!

33. Toggle guide Next toggle pair After Frequency sensitive filtering is destructive in its nature. It discriminates against “ slopes ” it does not like. It is far better to predict noise patterns then gently lift them off, bringing out the energy which lies below. The success of this noise removal algorithm depends on this virtue. Unfortunately this data had been subjected to traditional band pass filtering before stack. Keep toggling!

34. Toggle guide Before 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.

35. Toggle guide Next toggle pair After 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.

36. Toggle guide Before While you will see here that the system lifted off a good bit of the noise, it is not perfect, and the final output still leaves a lot to be desired. I do believe we could do a better job working with raw data. Keep toggling!

37. Toggle guide Next toggle pair After While you will see here that the system lifted off a good bit of the noise, it is not perfect, and the final output still leaves a lot to be desired. I do believe we could do a better job working with raw data. Keep toggling!

38. Toggle guide Before Watch what happens to this noise.

39. Toggle guide Next toggle pair After Watch what happens to this noise. The resolution now shows the blurring was caused by overlap. Not perfect, but certainly better.

40. Toggle guide Before

41. Toggle guide Next toggle pair After

42. Toggle guide Before Last before some preliminary results (but worth some serious toggling). Shown on the next series are the straight stacks of every 20 de-noised in-lines coupled with their inverted and integrated partners.

43. Toggle guide After Last before some preliminary results (but worth some serious toggling). Shown on the next series are the straight stacks of every 20 de-noised in-lines coupled with their inverted and integrated partners.

44. 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.

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

46. 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.

47. 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.

48. 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.

49. 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.

50. 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.

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

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

53. 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.

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

55. Excuse me!

56. Again the amplitude factor.

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