Some background and a few basics -

How my inversion works – and why it is better -

How added resolution makes parallel fault picking a possibility –

The noise – A classic case of shallow critical angle crossover.

Compendium menu and access instructions.

A summary supporting use of time domain optimization in inversion and noise removal -Before you start criticizing details on my strike slip fault interpretation let me say you probably would never have even known any were there before my system brought them out. Further, until you take the time to consider the points I make in this show, you probably aren’t qualified to interpret them. In any case, perfection is not needed to make the point.

Since strike slip movement is horizontal, often no vertical throw will be seen on a section. In addition, pre-stack migration has blurred all breaks. Thus Traditional fault picking guidelines don’t apply. This is a simulated sonic log section. Input traces were inverted and the spikes integrated. The industry has ignored The fact that such simulation of lithology is needed to make sense out the jumble of primary event amplitudes. The bright red event now can be considered direct reservoir evidence. So please follow the click-able guide.

The return here icon

On the seismic conflict between closed (proven) equation sets and statistical optimization.To understand why non-linear optimization techniques have not yet won the seismic processing battle, one must consider some history. About the time I wrote the first predictive deconvolution at Western Geo., a brilliant team from MIT came up with a way to describe seismic events in terms of frequencies and phase. Until this, mathematicians had not been able to build linear formula combos that would work in the time domain. This transformation allowed them to write closed equations to attack seismic problems, and the process seemed so logical it took seismic R&D by storm. Most geoscientists consider themselves mathematicians, and as time wore on the beauty of the time series concept won out over my time domain system. After all – if it can be proved mathematically it must be true (they said), and parallel comparisons were (and are) rare. However, unless I am mistaken, it should be noted that these all important transforms operate in the time domain.

I start with a several basics to set the stage -

The first of these is that the seismic energy continuum consists of thousands of independent primary reflections, each coming from a single reflecting interface(like from the top and the bottom of a bed).. These individual primary reflections do not mix in the subsurface. Earth filtering creates trailing lobes, the dominant frequency of those lobes decreasing with time. Because of the .huge difference in to and fro travel time between traces, this filtering creates big differences in primary reflection wave shape.

The second (but associated) fact is that this travel difference shifts the relationship between primaries . To illustrate the importance of this, suppose the second lobe of the top primary is lined up with the first lobe of the bottom. The fact that the two primaries have opposite polarity means that a lime might look like a gas sand. Of course all variations in between will occur. In any case, the composite wavelet shape that emerges from each “ recorder stack” is going to vary all over the place.

And now a general statement that should be self evident. While a normal seismic section shows general structure, it does not represent actual bed lithology – each event we see on the gathers is the result of an almost accidental, crude stack of individual primary interface reflections as they arrive at the geophones. The final stack further confuses the wave shape determination. These unfortunate realities explain why integration is an absolute necessity before we can trust calculated attributes. They also cast severe doubt on AVO theory. The trouble is that true integration can only be done on spiked results. My system is already there, but to spike in the frequency and phase domains would require the transforms to rather exactly model the average wave shape. Because this is so difficult, these routines previously had to settle for just shortening spectra, which is not good enough.

Currently, frequency / phase systems, in order to overcome the transform problem, are incorporating statistical optimization. At least that puts us on a more level playing field, with emphasis shifting to my area of expertise, and it now becomes a contest with higher goals. I discuss this further on the next topic.

Again, to emphasize the importance of integration, I show an example on the next slide.

Input stack Simulated lithology What happened here –

1. The system computed the reflection coefficients (spikes) on the input stacked trace (the inversion).

2. It integrated them, did a low frequency correction, and then displayed the results.

In this simple case, the input spikes consisted of the blue top, the blue bottom, the red top and the red bottom plus weaker deep ones. The vital thing here is how it got the thickness right.

This was a shale play. The upper detail is from the top of the shale section. The interfaces are weaker, but the system seemed to accurately portray the thicknesses.

Follow the blue link below to show with dozens of great well matches,

The true power of statistical optimization was unknown to me until I started pushing it hard. In my original predictive deconvolution I had used the autocorrelation extensively to get a guess at wave shape. Then, one day, working on my new inversion, I decided to see if I could improve my initial wavelet guess. The fresh idea here was to use it to make a convolution pass, record the spike guess timings and go back to use this information to compute a new wavelet. It soon became evident that each new wavelet explained the trace energy better than the last. Thousands of hours later the system was showing me the statistical power was there . My philosophy was to ignore the time it took and just concentrate on how deep I could go. I must say I was continually surprised, (These are the kinds of things one can do when one does not answer to anyone else). Of course I evolved tests that allowed the system to exit the loop if the improvement was not significant

The operating theory of my inversion is to keep making wavelet and spike position guesses until the original trace energy is explained to the limit of system ability. The logic consists of 3 layers of iteration. The first (open ended) level loops through consecutive wavelet guess runs, starting with an initial wavelet that is computed using autocorrelation like logic

The 2nd 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. 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. Each guess is displayed during the run, and watching the shape develop is an education in itself.

Obviously I developed some driving logic tricks to push the convergence but the system is as honest as it can get. It can display the spiked output, and if you understand what you are looking at, it is impressive. I soon learned however that potential users did not like the complexity of multiple interface interpretation so I went to the integration and soon became convinced that it in itself was a major contribution. It still puzzles me that this truth does not seem to excite interpreters, but I feel the same on the other major points.

This is what I mean by getting the best answer possible. Where formal mathematics have to give up, I can still get a fair set of spikes. This can be looked at as the ability to shift the boundary error to one that statistics can handle. The reason non –linear approaches like mine can beat the frequency/phase methods is they have the freedom needed to truly harness the amazing power of statistical optimization. The ability to build the required logic into formulae that can be proven mathematically is flashy, but solving the wavelet shape problem is the important key. Obviously the logic still has to be mathematically sound, but perhaps there is a higher level that more effectively combines statistics into the fold.

Strike slip (parallel throw) faulting should be accepted as a geological fact.Yet it went un-noticed for years because seismic resolution was not good enough for us to see the fault patterns.

I was the first in my knowledge circle to start picking them. I met with much amusement on my ignorance (and this was just a few years ago.) I say the reason I began seeing them was because of the increased resolution I was getting with my sonic log simulation.

The reason one should expect them all over the world is that they accompany the tearing caused by continental drift (and its deep plate movement). One cannot believe drift and not accept the consequences.

They are hard to see because horizontal movement may not create any vertical thow, and even if it does, stratigraphic interval layering may change, making the visual spotting even more difficult. Fault A, for example is upthrown (at the right) at the top, and downthrown at the bottom.

Sonic log simulation provides better cross-fault correlation, as well as emphasizing abrupt changes in character. Some of these attributes are very subtle, and require visual study. PowerPoint series covering a span of either in or cross lines help the interpreter establish the overall pattern. Once that is checked out, the minor details become quite obvious.

This is Gulf Coast data, where it has become obvious that strike slip faults control reservoir boundaries. How important does make all of this attention to detail?

4

Coherent noise - I start with a North Sea gather example that started me on this theme of refractions caused by critical angle crossings. On the next slide I show the same kind of thing on the gulf coast line used for this show.

At the red arrow you can see an early case of an event cloning into a refraction (note the over-correction indicating horizontal travel). Farther out you see an explosion of energy. This was created by the pre-stack migration logic that could not handle refractions. The same gather data without pre-stack migration showed no such problem. Since there was little need for migration in the first place, this is a heavy price that is commonly paid.

At the white arrow is the chalk horizon. The same thing happens here, but in spades.

The reason this is so important should be evident to any geoscientist. I am sure you have noted it, but I will point it out anyhow. It is that once the critical angle is crossed, all downward energy is halted, and refraction noise takes over. Here, at least half of the offsets contribute nothing but trouble. I feel confident, from what I have seen, that this phenomenon is quite common, all over the world.

By the way, the trace at the immediate left is the stack. The raw power of the stack to bring out the common denominator continually impresses me. Of course that common property is the correct NMO, but in cases like this it is a hopeless battle..

Amplitude v.s. Offset – We are here to discuss the coherent noise generated by this critical angle crossover. However I can’t resist reminding all that AVO logic would have accidentally spotted the bright spot because of the impossibly high amplitudes caused by the wave trap. To say their theory had anything to do with what we see here is nonsense. This phenomenon is repeated over and over again in diverse areas, and that probably explains the success rate we hear about. Being a “black box” advocate I can’t knock those results even if they come from ignorance of the facts. However I think the great stratigraphic detail we got with our deep mute beats what they can do. Now, if we had the raw data needed to lift this observed noise off, we might really see some astounding improvement in lithologic resolution (and that would be true in many other areas).

And here it is off the gulf coast. The offending event is the red producer seen on the first slide.

An extremely deep mute was needed to get the quality you see there.

Introduction – take a minute to see where I am coming from and why this might be worth your time.

Then look at some great well log matches to see the merits of non-linear optimization.

Or some seismic basics all interpreters should be aware of.

And now look at some sources of seismic noise

And a quick look at refractions spawned by critical angle crossing.

Now to the results of noise removal on a deep South Louisiana project.

Or back up to look at the system in action on this last one. Sit back and watch the timed slides.

Or here to the results from seemingly hopeless Permian basin data.

Or here to still another example of down wave truncation.

Or here to where I first identified strike slip faulting on a North Sea project.

Or here to a Gulf Coast strike slip fault example.

Or here where I discuss direct reservoir detection.

Or here for a different discussion of intertwined signal and noise.

Or here for a different twist on why ignored noise saved prospects for newcomers.

Or here for a more complete noise primer.

Or here for a comprehensive look at my inversion.

Or here for another look at well log matches.

Or here for a fairly sarcastic look at near/middle/far stack options and a wrap-up.

This is the router in Paige’s set of non-linear seismic thoughts. If you were there, browsing through would be super fast and simple. To get there, see next slide.

I have spent a good bit of time collecting PowerPoints into a folder, which I have sent to my FTP site. If you are interested, You have to do the following to access the work.

1. Enter " adaps.exavault.com " in your browser and go there. The username is adaps and the password is adaps1 ..

2. Select the folder PN and "download all". It sends a zipped file.

Create a new folder on your PC named PN, unzip and load the two files (shows and base.ppsx) into PN.

3 access "base.ppsx" and you will get the router which will lead you to all the others. This eliminates the load time problem.

At this fairly late time I have no idea if anyone is accessing the shows the way I had hoped. Just going into the list blindly is not as productive, since the menu puts them into a more reasonable form. Communication can be lonely.

Thanks in advance

Dave Paige