A siliciclastic gas reservoir is analyzed through two seismic attributes, root mean square (RMS) ampli-tude and reservoir to shadow mono frequency (RSMF) to detect and quantify porosity and gas accumu-lation. The objectives are to apply forward modeling to design the interval for amplitude mapping to de-lineate porosity; to analyze the amplitude of the frequency to generate the RSMF map; and to validate the RMS amplitude map with porosity, and the RSMF map with gas test results.
One well was modeled for two cases, a tight and porous reservoir, to establish an appropriate RMS amplitude extraction window for reservoir porosity. The amplitude around the reservoir was extracted and validated by wells. The seismic volume is decomposed in the frequency domain for the gas detec-tion technique. This technique measures the amplitude attenuation in the dominant frequency between the reservoir level and the zone just beneath it to produce the RSMF map, with the assumption that the frequency amplitude attenuation increases while passing through the gas reservoir. The RSMF map was validated by the normalized gas test results of several wells.
The forward modeling indicates that up to 20 ms below the reservoir is an optimum window to corre-late the targeted reservoir with the RMS amplitude. The RMS amplitude was found to be directly pro-portional to porosity, with the modeled tight reservoir case having lower RMS amplitude than the porous reservoir case within the optimum window. The RMS amplitude response for the modeled tight reservoir case almost matches the in-situ case. There is a correlation coefficient of approximately 70% between the average reservoir porosity of the wells and the RMS amplitude map extracted from the seismic volume within the optimum window. At the dominant frequency of 20 Hz, a correlation coeffi-cient of around 82% is observed between the gas test results and amplitude attenuation. The RMS and RSMF attributes are affected by seismic amplitude preservation as well as signal-to-noise ratio, which can impact the level of uncertainty between these two attributes and the extracted maps.
The integration of RMS amplitude and RSMF attributes complemented the characterization of the si-liciclastic gas reservoir for detecting high porosity zones with gas accumulations. Zones with high RMS amplitude and attenuation were highlighted to be targeted for well placement.