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Land amplitude attributes are suspect: what interpreters need to know about seismic processing and QC
By: Christof Stork
Processors are very, very good at giving the interpreter what he wants for the price he demands. But, are interpreters deceiving themselves about the reliability of pre-stack amplitudes, especially in noisy areas? Interpreters can do a lot of with pre-stack amplitude attributes if the data are reliable, but has the business impact of amplitude attributes been limited by unreliable data?There is much room for improving the reliability of pre-stack amplitude attributes. But, the current business relationship and expectations between interpreters and processors hinders improvements in this area. Is it in a processor's best interest to advise an interpreter that his data is poor, processing will affect amplitudes, and the interpreter may be trying to do too much with the processed pre-stack amplitudes? Processors are in a difficult position.Interpreters need to know more about processing and need to change what is acceptable QC from processing. This will require more work by the processors, better communication with processors, and will require interpreters to pay processors more. But, without it, interpreters may be deceiving themselves.I'll show how interpreters can be deceiving themselves, what interpreters should know about processing, and what improved QC to run in processing for pre-stack amplitude objectives. A main concern is about "threshold filtering methods", which are powerful and necessary in some cases, but are used too much because they deceptively produce results that interpreters like. Furthermore, the common before-after-difference QC approach can be misleading. I suggest we should be more skeptical of seismic data quality and we can be more aggressive in QCing processing results. Pre-stack amplitude attributes have a lot of potential. We need for seismic data to be better. Interpreters need to drive the improvements because they are the customers. I'll show that there is opportunity for improving seismic data.
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