Because noise removal is central to my later work, I start with a discussion on how intertwined coherent noise creates a random effect that confuses all frequency domain calculations and generally screws up the works – Pay close attention here, since this is a vital point.

To the left we have a raw gather and to the right the same data with noise lifted.

The fact is such noise is present on every prospect, to some degree. The problem is seeing it! To do so requires intense pattern searching on the gathers, and this is not normally done. When we add the factors I outline next, such things as AVO claims and frequency domain waveform generation become questionable, and the need for non-linear approaches becomes apparent.

1. The seismic energy continuum consists of thousands of independent primary reflections, each coming from a single reflecting interface.

2. There is no mixing of primaries in the subsurface.

3. Geophones can only record the energy that exists at instants of time, and this forces an accidental form of compositing at the recording point. In essence this is a preliminary stack, and we have no control over it.

4. The earth filter generates trailing lobes with travel, gradually emphasizing lower frequencies. The total travel difference from inner to outer station is great and by the time the energy reaches the recording points there will be significant differences in primary wave shapes. This (mostly ignored) problem is exacerbated by excessively long spreads. It heavily affects gather trace character, probably dwarfing any possible AVO effect.

5. The separation between primaries changes with offset, modifying the way they combine at the recording point, again affecting gather trace character. As with the earth filter, it happens before any processing can be done. In summary, the final stack is an almost accidental waveform mixture.

For the reasons listed below the final stack is adding different waveform shapes. Thus

the need to invert before this distortion happens.

Some thoughts on frequency oriented inversions - The frequency domains were essentially invented mathematically to make solutions by equations possible. The new tool was the transform, which models what happens in the time domain into this new form. This conversion enabled the designer to invoke equations to generate filters that change the spectrum of the data to equal a desired one. This was as far as their early deconvolutions went. Of course these efforts were attempts at inversion. They just had to be limited to keep the processes stable.

The problem with going farther is that any particular spectrum can represent a variety of wave shapes. Later phase work has concentrated on determining that shape. This is the weak link in their process. When noise is present it gets much harder. My non-linear approach by-passes this problem by determining spike location via pattern recognition. The great well log matches I have shown pretty well prove the validity of this approach.

I continue with a graphic that tries to put the basic seismic problem into perspective.

shale

shale

Lime

Sand

The argument for non-linear methods.

1 The geology

2. The reflection coefficients (spikes in non-linear lingo).

3. The down wave 4. Its directionComputing reflection coefficient spikes via statistical optimization eliminates frequency and phase from the picture.

The crazy down-wave at the left is just there to show the nodular character that evolves with depth. The “shape” of the primary reflections at each offset, as well as the offsets between them, will depend on the total distance traveled. They are stacked at the receiver location, and the resulting trace character will vary greatly between offsets.

Rigid, mathematical solutions of complex problems like this are extremely tough. Optimization is typically the answer when coming up with the best answer possible

is what we want. Because linear inversions are not able to compute reflection coefficients without knowing the wave shape, the industry has become obsessed with that problem, with opinions coming from everywhere.

I use the oval to emphasize that this is what inversion should be doing. Once we have effectively computed the reflection coefficient spikes we have raised seismic to the well log level. This is where my non-linear inversion takes us, avoiding the exact wave shape hurdle by calculating spike position via pattern recognition. We’re looking at completely different approaches, and the way statistical optimization can handle error is the key to being able to get answers under difficult circumstances. Again, the proof lies in the well log match.

Processes should be judged by results - One could spend hours studying what there is to learn from this display of a “simulated sonic log” section. First, notice how bed thicknesses make the “stratigraphic differences” across the strike slip faults very evident. To me, as an interpreter, this is vital.

When I re-look at this picture I shake my head and wonder why almost no interest has been shown. Nobody had noticed them before I made this run and I guess they will continue to be ignored.

Srike slip faults are a result of shallower beds being torn apart as a result of deep plate movement (continental drift). They are a given fact of geological life. The reason they have been missed by the industry is that they often are very hard to see. Where the stratigraphy is regionally regular, no vertical throw may result, making patterns very hard to establish. When one can’t see a pattern between the bursts of energy, it is normal to assume we are looking at noise.

Geologic sense is key to validating this interpretation. Each fault block in this pattern exhibits stratigraphic consistency within itself. Before the pattern was set it looked like an unconnected hodgepodge. Much of my later efforts to improve resolution were prompted by the need to see the fault patterns I was sure existed. My time domain inversion and subsequent sonic log simulation are vital parts of this improvement, and that Is why I place this subject first on the list.

Next you might observe that the changes in amplitude now seem to make structural trapping sense. This ties into my claim that direct reservoir spotting is now a good possibility!

For more, click on oval -

Raw seismic sections are “coincidental” mixtures of primary reflections.The amplitudes and polarities of stacked events depends on how the primary reflections were aligned when they reached the recorder.

These alignments depend on effective velocities (a function of distance from source to receiver). Earth filtering continually produces trailing side lobes with distance traveled. Because of large differences in recording travel time, these character changes can be significant. Of course they occur before any processing is possible.

In this mix, the strong will prevail. The amplitude of each primary reflection is a function of both the velocity of the previous layer, and that of the current one. Thus the lower interface between a sand and a limestone will generate a strong event,making the sand look like a lime, where that between a sand and a shale will be weaker. Of course the same is true on the upper interface. The point here is that trusting the amplitude of any stacked event before inverted data is integrated can be specious, at best. In my own development work I was continually surprised (and pleased) at how the integrated results matched with available well logs. Not perfect, but certainly a big improvement.

The simulated sonic log section at the left is a good example of where integration after inversion made good sense out of amplitudes. The well match on this strong red event matched beautifully, to the point we could have predicted success if the run had been made before drilling.

Unfortunately, due to me being isolated from the Ikon client, no one ever saw these visual results.

For more on simulation, click oval -

Stack (input) Inverted & integrated

This simulated sonic log looks too good to be true (but it is, and all the other ones we have matched at least come close to this quality).

Once again, we inverted down to the reflection coefficient (spike), then integrated the results.

While it looks great to me, many seem to have trouble adjusting to the squarish nature, not realizing that was the goal all along. Those thicknesses are crucial to long range seismic correlations, and to detailed stratigraphy.

The curves are not sinusoids, and frequency analysis does not apply in any sense. What they really are is truth and beauty, thank you.

If you need to see more, click on the oval -

Now let’s talk about noise removal – To begin, it is vitally important that we lift it off gently, so as not to disturb the underlying signal we are trying to bring out. Obviously frequency sensitive filtering is a no no. Predicting the individual noise events and computing correlation coefficients is the key. The example comparison below proves that can be done. For more, click on oval -

I refer to the fact that all sorts of academic assumptions have been made that ignore the effect of noise inter-twining with signal.

The next series of pictures introduces several shows I believe are important. After moving through the possibilities, come back and click on the image that interests you, giving the PowerPoint plenty of time to load. Since you might have trouble getting back here, it would not hurt to bookmark this file.

This show is my latest attempt to explain my work. It uses a set of data that was giving the geophysicist problems. Click on the oval here for the PowerPoint -

Click on oval for direct reservoir detection -

http://postle.ppsx/

This south Louisiana work is perhaps the one I am most proud of. It really was the beginning of my serious noise removal efforts. The flank events butting up against the salt dome did not even show up before the noise was removed. My advanced scanning for noise events was developed here, I was able to track strike slip faults on theboth ends of the dome, virtually proving that they contributed to the actual formation.

I started here with data in the shot point format, with no NMO applied. This allowed a more precise and logical scanning to detect non reflection NMOs.

The system removed so many refraction events (stemming from the central noise cone) that I wondered if there could be any energy left. The results showed that there was.

There are more things to talk about but I will leave it here for now. Click on oval below to go to router.

Click on oval to enter this one.