simple seismic attributes analysis methodology

It is important to reduce noise effect and increase the signal to noise ratio, to enhance seismic data started by quality control to study how noise effect on seismic data and generate spectrum analysis to determine amount of noise and seismic bandwidth frequencies. Then, apply band pass filter to remove high frequency and low frequency noises and mean smooth filter to overcome random noises that affecting on the interested seismic data band frequencies. After data preconditions applied similarity attributes have been used to extract geobodies by available autopicking algorithms.It is hard to determine the best workflow for geophysical study so we need to merge experience with latest technology to solve complex geological problems and enhance results of seismic interpretation. In this paper, it is a trial to combine both conventional and unconventional seismic interpretations to extract all available geological information from seismic data as shown in Fig. 2 that represents seismic interpretations workflow that enables interpreter to reduce risk and get results very fast by mixing experience interpretation with latest technology.

Data Preconditions Workflow

Sometimes seismic interpreters have chance to check seismic processing workflow in this case they can check every step in seismic processing workflow and check for seismic attributes result, the best way to quality control (QC) seismic processing workflow by generate similarity (coherency) attributes in every step and compares result before and after making QC for seismic processing steps .

If we do not have chance to check seismic processing steps it is recommended to enhance poststack data before seismic attributes generation, using structure orientation filter and bandpass filter. Interpreter can enhance seismic attributes results, before start poststack processing it is important to check seismic data to increase signal to noise ratio by generating seismic data preconditions workflow.

There are two types of filters used in the poststack data preconditions: 1) Filter without edge preserve to smooth seismic data and not stop in geological features edges like faults and channels and 2) Filter with edge preserve (structure orientation filter) to smooth seismic data and stop at edges of geological features.

Three steps have been generated in this work for effective data preconditions techniques .  

1)   Seismic data quality control by spectrum analysis relation between frequencies and amplitude in 3D seismic cube we can determine interested bandwidth frequencies, 

2)   Band pass frequencies filter to remove low and high noise frequencies, and

3)   Overcome random noises by smooth mean filter, the mean filter is a low-pass filter that typically is implemented as a running window-average filter

Edge Attributes (Discontinuity Attributes)

Variance: Uses statistical squared differences of adjacent (trace by trace) seismic amplitudes.

Coherency: Dot product cross-correlation of “adjacent” waveform (phase, frequency and amplitude) packets.

a)    Semblance: Computes the squared sum of vectors along the trace and off the trace, the maximum sum direction has most semblances (Fig. 5).

b)   Similarity: Checks a standard pattern of points around a central point for the most similar seismic amplitude and progresses to the most similar point as the next central point

Hypothetical horizon showing incoherent 

character illustrated by the lateral change in waveform character. b) A hypothetical horizon showing 

coherent character, marked by no lateral change in shape of the waveform. Plots above both figures show coherence response. (Modified after Salamoff, 2006).

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