عنوان مقاله [English]
Faults are among the most important geological events for hydrocarbon and mineral exploration and geological studies. They are considered as the major hydrocarbon traps whose detection in seismic reflection data have important roles in hydrocarbon reservoir characterization and geomechanical studies of reservoirs. According to previous studies, approximately 75% of the oil fields are associated with faults. Faults can influence the efficiency of hydrocarbon reservoirs by improving the permeability of a porous medium. Faults are defined as a discontinuity along a geological layer or geological event which is the result of the failure of that layer or event against the tension exerted on it.
Reflection seismology is one of the best geophysical methods for hydrocarbon exploration. Various methods have been introduced to identify faults in seismic reflection data. In seismic reflection data, faults can be seen as discontinuities along the reflectors. One of the conventional methods of interpretation of the faults is a manual interpretation of seismic horizons which is a highly time-consuming process. Due to the nature of the seismic data, identification of faults plane in this data is a difficult process.
Seismic attributes are considered as useful tools that will reveal hidden information in seismic data which can help the interpreters to detect faults. In fact, a seismic attribute can be used as a filter that makes the structural and stratigraphic information more apparent from the seismic data. There are several seismic attributes for faults detection in reflection seismic data such as coherency, curvature, chaos, and variance. The seismic coherence attribute is one of the most common attributes for fault detection. This attribute is calculated by different criteria such as cross-correlation, semblance, eigenstructure, gradient structure tensor.
In this paper, we used a new form of a seismic attribute for edge detection. It is the Sobel filter which is a widely used tool in image processing, computer vision, and edge detection problems. It is the first derivative of the image and is sensitive to amplitude changes. It is a discrete differentiation operator, computing an approximation of the gradient of the image intensity function by convolving the isotropic operators. These operators are extensible to higher sizes and dimensions. Therefore, the Sobel attributes can be used in two- and three-dimensional seismic data. It can be used in seismic data for identification of the geological events such as faults, salt dome, and buried channels.
The efficiency of dip guided Sobel attribute is evaluated by applying it to both synthetic and real seismic data. We compared the obtained results with the results of conventional seismic coherency attributes. In this paper, we used the eigenstructure-based coherency attribute. Comparison of the results of synthetic models in both noise-free and noisy cases in two and three dimensions show that the dip guided Sobel filter can be a good alternative for coherence attributes. In comparison with the coherence attributes, dip guided Sobel attribute is a short run time process and has large stability against noise. In real seismic tests in two and three dimensions, the obtained results from two attributes show that the dip guided Sobel attribute performs better than the eigenstructure-based coherency attribute.