Seismic Analysis Using Wavelet Transform for Hydrocarbon Detection
Many hydrocarbon detection techniques have been developed for decades and one of the most efficient techniques for hydrocarbon exploration in recent years is well known as amplitude versus offset analysis (AVO). However, AVO analysis does not always result in successful hydrocarbon finds because abnormal seismic amplitude variations can sometimes be caused by other factors, such as alternative lithology and residual hydrocarbons in certain depositional environments. Furthermore, not all gas fields are associated with obvious AVO anomalies. Therefore, new techniques should be applied to combine with AVO for hydrocarbon detection. In my thesis, I, through case studies, intend to investigate and validate the wave decomposition technique as a new tool for hydrocarbon detection which decomposes seismic wave into different frequency contents and may help identify better the amplitude anomalies associated with hydrocarbon occurrence for each frequency due to seismic attenuation. The wavelet decomposition analysis technique has been applied in two geological settings in my study: clastic reservoir and carbonate reservoir. Results from both cases indicate that the wavelet decomposition analysis technique can be used for hydrocarbon detection effectively if the seismic data quality is good. This technique can be directly applied to the processed 2D and 3D pre-stack/post-stack data sets (1) to detect hydrocarbon zones in both clastic and carbonate reservoirs by analyzing the low frequency signals in the decomposed domain and (2) to identify thin beds by analyzing the high frequency signals in the decomposed domain. In favorable cases, the method may possibly help separate oil from water in high-porosity and high-permeability carbonate reservoirs deeply buried underground. Therefore, the wavelet analysis would be a powerful tool to assist geological interpretation and to reduce risk for hydrocarbon exploration.
Cai, Rui (2010). Seismic Analysis Using Wavelet Transform for Hydrocarbon Detection. Master's thesis, Texas A&M University. Available electronically from