Without a good understanding of the faults and fractures present in a net pay zone, the possibility of wasting valuable resources is high. We characterize here fractures and faults within the Utica Shale by integrating routinely used methods such as geometric attributes (e.g. Dip, Similarity and Curvature) and comparing them with a new fault attribute that extracts faults and fractures, and improves their visibility. The new method also helps minimize random noise in the seismic data. In order to fully optimize faults and structures, we first filtered the seismic data with a structurally oriented filter to reduce the noise and improve the imaging quality. Using a single attribute to derive information from faults and fractures is not optimum, therefore we employed a second step, applying several conventional attributes such as similarity, curvature, and fault enhance filters. These successfully identified the fault and fracture geometries. A comparatively new fault attribute, known as Fault Likelihood and defined as a power of semblance, was then used to capture and delineate faults and fractures in the same Utica Shale area. This attribute is created by scanning a range of fault dips to identify maximum likelihood. The value range of the fault likelihood attribute is between 0 and 1. In order to obtain even sharper fault plane, a filtering step is also performed. When compared to traditional attributes, the faults and fractures are better defined by the new method. In addition, the new fault likelihood attribute is extremely versatile and can be used to characterize fault and fracture proximity and density.
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