Another example of critical angle refraction noise. No noise removal With noise removal.

You saw the sonic log version You saw the sonic log version. These are the input stacks – subtle but important differences. My goal here was to see how much I could improve, to better pick what I know are important faults. No noise removal With noise removal.

This show is part of a series that deals with improving seismic resolution through non-linear methods. In a sense the discipline of constructing the displays is, in itself, a development tool. Before After My work always starts with gathers. Before and after noise removal pictures are shown for two depth points, on each slide. I show a number of these slides, inserting explanations as se go. If you stay with me I will discuss the noise origin in some detail, and point to specific examples (as above). Before After

You will notice the only usable energy is on the very inside traces You will notice the only usable energy is on the very inside traces. I have looked at the entire 3D project and can assure you this is true through it all. Before After I say the cause was the fact that early “critical angle crossovers” interrupted the downwave. Much of the noise you will see are refractions spawned from strong beds (where their reflection was terminated). This type of noise is very common (and completely ignored). It certainly brings out the AVO advocates, who follow their own path. Before After

Since refractions travel horizontally, difference in trace to trace position is a linear function of their velocity. Thus normal moveout over-corrects. My logic searches them out and then lifts them off. Before After While there is a distinct improvement on the final results, the fact that it is not “day and night” is a tribute to the raw power of the stack (which is “looking through” the refraction noise). The importance of establishing the reality of this noise stands by itself however, since so many mistaken concepts (that it throws out) have gone on for years. Before After

Let me go back over the critical angle thing one more time. Before After The angle at which all downward traveling energy is cut off depends on the reflection coefficient and the interface depth. Nature abhors a vacuum, so every time a strong reflection is abruptly interrupted it spews off a refraction. While noise can be hard for the human eye to spot, you will hopefully see a number of of noise cases where the refraction “hangs” on the reflection. The system catches most of them, but nothing is perfect. Before After

Of course refractions are not the only under-treated noise type. Before After We also scan for under corrected events. The two types are inter-bed multiples and low velocity shear wave reflections. If you watch closely you might see a couple of examples of those ignored noise types. Before After

In a logical world, this single slide should prove the point. Here is a great example of refractions hanging on reflections. Before After In a logical world, this single slide should prove the point. Before After

To track the boundary between refractions and real events Before After Would require a sophisticated ray tracing study, as shown above. Before After

But for now I just add a few more example slides for emphasis. Before After When finished with these I show a fault study on another in-line. Before After

1. However, since I have a lot to say, I’ll use the space to develop my main themes. Before After In my extensive work with sonic log simulation (with it’s myriad of great well log matches) I have developed a faith in the validity of amplitude as a valid hydrocarbon indicator. Working in strike slip fault areas I found that abrupt amplitude changes on my simulations provide necessary clues for establishing believable fault patterns. Before After

2. But it soon became apparent that noise seriously compromised my results. Before After In my earlier processing of this data, I saw the noise, but had not yet developed removal tools. I thought I could establish a fault pattern, but my results were not good enough for confidence. Since I firmly believe decent fault traps have been missed by others, improving the results became a means to the desired interpretation end. Before After

3. The heavy blurring effect of pre-stack migration has seriously hurt fault picking. Before After As a result we are most dependent on seeing changes in character as fault evidence. The ability of my simulation logic to make sense of the set of primary reflections (that come from a bed) is key. In addition I am convinced that strike slip (parallel) faulting is omnipresent. The fact that no real vertical offsets are necessary is a big problem. Before After

4. So, on the next slide I show another in-line comparison for your study. Before After I then repeat that comparison, to show how I pick one of the faults I see. In summary, the entire effort is oriented to this type of interpretation. It is my recurrent theme and avocation. Before After

No noise removal With noise removal.

No noise removal With noise removal.