Hydrocarbon Identification Based on Bright Spot Technique by Using Matching Pursuit and RGB Blending

Abstract

In seismic exploration, matching pursuit (MP), which decomposes wavelets based on best signal matching, can not only extract accurate frequency information, but also can pick up the strong amplitudes of possible bright spots adaptively according to the threshold or iteration control. RGB (Red-Green-Blue) blending technique of spectral bands can make full use of the information of all frequency bands, which can reflect the general frequency changes of seismic data reducing the interpretation ambiguity. Therefore, in order to improve the precision of interpretation based on bright spot technique, the authors take both amplitude and frequency into account and propose a new approach for hydrocarbon identification by combining both methods. First, we choose the decomposed wavelets of strong amplitudes related to hydrocarbons in MP algorithm to predict the possible bright spots, so as to remove the effect of unrelated reflections in the bright spots interpretation. Then, RGB blending technique is used to the low-, mid-, and high-frequency spectral bands of the bright spot prediction section to assist for the hydrocarbon identification. Finally, the validity of the method is verified by both the model and field data tests. Results demonstrate that the proposed method can improve the precision of hydrocarbon identification, and reduce the interpretation ambiguity of the reservoirs.

Keywords

• Hydrocarbon identification
• bright spot prediction section
• matching pursuit
• RGB blending
• frequency-divisional attributes