Can we see shale fractures? Some claim to already doing it but I have my doubts. I believe I’m close, but not there yet. I’d like opinions. The section below has been inverted and integrated from the optimized stack above. It is from one of the original shale plays. In the circled area the blue is the target shale. You can see it matching nicely with the sonic log overlay. What we would like to see is a nice sharp pickable break in that image. It may be we will have to settle for much less, but that is the question. In any case, if we are going to even pretend that we can achieve this kind of accuracy, we will have to supply the pertinent detection software with more statistical information, and this will have to come from extra gather traces inside the normal moveout pattern. Obviously we would be interpreting the section from which this clip was taken, looking for minute detail. Who is to say we might not already be seeing hints of what we are looking for? 2D testing – Since all my noise removal, inversion and integration is trace independent, the resolution I show here would be the same on a 2D version of this in-line. Thus any company that wants to join in further testing could do it on the cheap. I’ll process any such test free. Now on to the spread discussion.

Usable data Hypothetical target time. Common sense As applied to seismic spread design. At this hypothetical target time, under this hypothetical spread, we’d have 9 traces to play with, and this number would decrease going up. While this exaggerates, the odds are that less than half the traces in your 3D set are useable down to the limit of how deep you can afford to drill. Effective statistical optimization logic needs all it can get. Often, older sets use a tighter design, and the results from them would be superior to what you can get from newer shooting. Of course one has to assume having the extra traces will make a significant difference in results. Let me First show you an example of how my system used extra gather traces to detect & remove noise on a shallow (coal) project in Scotland. The fascinating thing about this comparison, is that one normally would have assumed there existed a severe statics problem just looking at the input. My noise removal logic iterates through a large set of velocity assumptions as it looks for both abnormal (multiple) arrivals and for refractions. On each iteration it predicts and removes the event it just identified. Again, the more traces it has to work with, the deeper it can go into the recorded data. I show several other examples on the following slides.

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. In my book, the definition of inversion is to solve for these reflector spikes. To do it we need to know the shape of the down wave for this period. The crazy picture at the left is just to show the nodular character that evolves with depth. The “shape” of the primary reflections at each offset, as well as the vertical shifts between them, will depend on the “down and up” distance traveled. These primaries are stacked by the receiver, the trace shape varying greatly between offsets. Once we have computed these “spikes”, we would like to move backwards to simulate the lithology we drew in at the upper left. If our answers are accurate enough, we can do this by integrating the spikes and making an educated low frequency correction. Of course that accuracy depends on how well we have cleaned up the gathers, and this depends on whether the spread design has given us enough gather traces to work with.

Mine has been, of course, a specialty processing operation. I call the result on the left an “optimized stack”. Noise has been removed, and the individual gather traces scaled on the basis of how much each contributes to the stack. Cross correlations are used in many ways. In effect, the data is nursed at every step, making sure that each stacked trace is completely independent. However, no inversion has been yet applied, so we are looking at a mixture of primary reflections and this falls short of simulating lithology. Again, the overall system thirsts for more gather traces to feed its optimization efforts. The fascinating thing about the inversion shown to the right, is how it has truly brought out structure that was not at all evident on the input. This is a new version that is now time variant. This means that determining the wavelet shape at each level is dependent on how many gather traces it can use (going back to the first slide). My latest inversion goes even further by collecting and inverting results from individual receiving offsets. Again the direction I have moved in almost demands shorter offset differences to get more traces.

MY ENTIRE FOCUS IS ON.LITHOLOGY- Predicting bed thickness from seismic got me excited a long time ago, and I still like to revisit some old results. Click to get me started on this example. This data is from one of the initial shale plays. Bringing out such detail in that type of lithology is difficult. I look at this and ask how much better the results might be if I had more traces to look at. The ongoing challenge here is to be able to see breaks in the shale, both before and after fracking. Hopefully some interruption of continuity will occur. It would be nice to see what could be done with twice the gather traces. Of course I would like to see any other systems do as well as mine has already done. Take some time to really study this example. Please visit for other non linear topics.