Goal is to develop and test a new methodology for using seismic attenuation attributes, from multi-component P-wave and S-wave seismic data, for assessing the presence of high oil or natural gas saturations, and estimating reservoir properties (in particular, gas saturation, lithology, porosity, etc). Deeply buried gas reservoirs along the Gulf of Mexico shelf are an important future energy resource for the U.S. One of the greatest problems encountered by operators in this area is identifying commercially viable targets for drilling. Because of their great depth (over 15,000 feet), the most common 3D seismic methods for direct hydrocarbon indication, such as amplitude versus offset (AVO) have proven unreliable in the past. The approach of this study combines three elements: (1) a synthesis of the latest rock physics understanding of how rock P-wave and S-wave inelasticity is related to rock type, pore fluid types, and pore microstructure, (2) synthetic seismic modeling that will help identify the relative contributions of scattering and intrinsic inelasticity to apparent Q attributes, and (3) robust algorithms that extract relative P- and S- wave attenuation attributes from seismic data. Recognizing the pitfalls of measuring attenuation in-situ, RSI will implement procedures that emphasize relative attenuation anomalies that are calibrated to well log information, and that incorporate empirical and theoretical constraints from rock physics. After developing the necessary methodologies for computing Q from downhole measurements and reflection seismic data, RSI will employ Artificial Neural Network and other methods to link the well-derived reservoir properties to the seismic attenuation attributes. This will provide a quantitative means to interpret Qp and Qs in terms of oil and gas saturation, lithology, porosity, and perhaps even permeability. This will be the ultimate accomplishment of this project. The developed methods will be tested using actual field data to be supplied by U.S.-based oil and gas companies. The data will be analyzed for indications of hydrocarbon accumulation and those results will be compared to data from ?blind? wells that will be withheld from the original analysis.