عنوان مقاله [English]
نویسندگان [English]چکیده [English]
Seismic attributes are very useful in seismic data interpretation. One of these attributes is the coherency attribute. Seismic coherency is a complex trace and a geometrical attribute that is applied to a 3D cube of seismic data. It is a measure of lateral changes in acoustic impedance caused by the variation in structure, stratigraphy, lithology, porosity, and the presence of hydrocarbons. When coherency attributes are applied to seismic data, they show the continuity between two or more traces of the seismic window. The rate of the seismic continuity is a sign of geological continuity. The 3D seismic coherency cube can be extremely effective in delineating faults. To calculate the attribute of coherency, there are three solutions: semblance, eigenstructure and cross correlation. Inputs of these algorithms are 3D seismic data. Similar traces are mapped with high coherence coefficients and dissimilar traces take lower coefficients. In this paper, we designed the semblance based coherency algorithm in Matlab and applied it to the synthetic data. For this purpose, we generated several 3D synthetic seismic cubes including micro-faulted horizontal, dipping, and cross dipping layers. We also studied the effect of the dominant frequency, signal to noise ratio and the size of the analysis cube in calculating coherency attributes. We used a Ricker wavelet with the dominant frequency of 30 Hz for horizontal layers and 35 Hz for dipping layers and signal to noise ratio of 1. We applied all three approaches of coherency attributes on a data set from the Khangiran gas field in NE Iran.
This method is employed using, as narrow as possible, a temporal window analysis typically determined by the highest usable frequency in the input seismic data. Near-vertical structural features, such as faults are better enhanced when using a longer temporal analysis window. By this algorithm, we were able to balance the conflicting requirements between maximizing lateral resolution and increasing S/N ratio. We studied the applicability of this algorithm to detect faults with minor-displacements and compared the results of this method with eigenstructure and cross correlation over the same data set. It provides better results compared with the other two methods. This study shows that the semblance-based coherency algorithm provides a better coherency cube than the eigenstructure-based coherency cube.