Visual interpretation is still the best. You can see reservoir possibilities that have been missed, and do it at a fraction of the normal cost. The tougher the stratigraphic problem, the better. A great way for managers to re-appraise leases. Bring the human brain back into interpretation via visualization of the true lithology. To understand the possibilities, you might have to revisit some seismic basics that contradict current practices. This might take some serious time, but what better things do you have to do (with drilling pressure off). This small sarcasm comes from the fact that I now am dipping into limited resources to feed my need to communicate. I know I am fortunate to feel this strongly about the profession I’ve buried myself in, and keep hoping I will find others that share this passionate interest in seismic logic.

The traditional seismic goal has been to produce contoured maps managers could use to pinpoint drilling locations. Time and horizon slices became popular tools here, all depending on the excruciating detail provided by 3D recording. There may be a few places in the world where horizons picked by current procedures truly follow stratigraphy, but they are the exception. The big problem however is that most of the valuable event information we now have the ability to see gets lost in this mapping process, along with the innate capability of the human brain to make sense of complex patterns. So the initial pitch here is on the merit of building multi slide shows that allow an interpreter fo regionally assess what is going on stratigraphically. New techniques now allow us to trust polarities and amplitudes to the point that the presence of hydrocarbons can now be predicted, especially when trapping structures agree. Once reservoir possibilities are seen, the detailed mapping makes some sense. On the first pass, using every 20 th in-line generally provides a cohesive picture. This alone cuts re-processing costs dramatically. Obviously, the higher the resolution, the more sure we can be of the reservoir predictions. There are so many factors that affect this resolution that I have put together this intro to a linked set of shows covering the more important ones. Please skim through the topics before choosing any individual, since getting back here sometimes even gives me a problem. Bookmarking would help. The capability I am most proud of is that of simulating the actual lithology, from seismic input that represents a conglomeration of individual primary reflections and various forms of coherent noise. Because the primaries come from all reflecting interfaces (tops and bottoms) their stacked results can be most misleading. Using time domain, non-linear methods, the ADAPS system first computes a set of reflection coefficient guesses, then integrates them to produce sonic log type sections. Because resolution is improved from this process, I was able to detect strike slip faults where they had been previously unseen. The next slide shows such a section, and the reason it comes first is that it illustrates the complete honesty of the inversion/integration logic. A little later I go through some of the seismic basics that have to be considered by all.

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. Strike 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. 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! First time In skim to see all subjects covered. For more on strike slip faults, come back & click on oval -

Because noise removal is central to my later work, I go on 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. (Remember to skim through before linking to a subject.) 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 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 direction Computing 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.

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. The proof of the industry need here is the fact that at least one reader thought my before should have been the after. It is easy to prove (from arrival patterns) that the events in the circle are noise. 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. AVO is a prime example, yet people swear by it. 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 -

In case any kind soul wonders what it would take for me to continue my work, it amounts to about the cost of a minimum wage employee. My goal is to keep playing with real live problems.

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 the both 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. Thanks for your attention. But look at this detail that was not visible before. Click on oval to enter this one.