FAULT AND FRACTURE IDENTIFICATION IN 3D SEISMIC DATA USING SEISMIC ATTRIBUTES

Abstract

Structural features like faults and fractures are very significant in understanding geology of a particular area. Faults hold lots of academic and economic significance. Faults and fractures can be the source of earthquakes. They can also act as structural traps for accumulation of hydrocarbons or can behave as migration pathways aiding the escape of hydrocarbons. Not only this, faults are prominent geological features in the continental slope environment where there is presence of the unconventional hydrocarbons like the gas hydrates. Therefore, from academic, economic as well as - operational safety point of view it is very important to identify and locate faults correctly. An 4. important step in this regard is to identify and locate faults and fractures in a seismic section as precisely as possible. There are various seismic attributes available which can identify faults using different edgedetection techniques. One way of fault identification is also by computing the coherence along the reflectors and identify faults (discontinuities in reflectors) as low coherence features. To compute the coherence too there are various techniques available (as discussed in Chopra and Ma,jurt, -. 2007). In my work, I emphasized on the eigenstructure based coherence estimation (Gersztenkorn and Marfurt, 1999). I used this concept as a basis to develop attributes in MATLAB which could be used to calculate coherence and further be used as a tool for fault identification. Accurate coherence estimation requires computation of reflector dips correctly. Therefore, I initially focused on developing two dip computing attributes in MATLAB which use Complex trace analysis method (Luo et a!, /996 and Barnes, 1996) and semblance based explicit dip scan method (Marfurt et a!, 1998) respectively as their basis. I discussed the important parameters of both these dip computing attributes. I also explored their impact on coherence estimation for fault ZA identification as well as their suitability for the same. Then, I developed the coherence calculating attribute in MATLAB which finally uses the computed dip to identify faults. Till now, eigenstructure based coherence estimation only uses the information obtained from eigenvalues. In my work, I tried to extend the scope of this coherence estimation to eigenvectors which supply fresh and independent information. I demonstrated that eigenvectors can also be used to identify faults. Through synthetic testing data and the field seismic data, I explained that eigenvectors are affected by different faulting scenarios. In fact, I demonstrated that the faults can -. be identified by eigenvectors in a better way than the traditional eigenvalue based coherence estimates for fault identification.