The ability to estimate water saturation for a thin-bed reservoir from seismic is greatly enhanced if two rockproperty transforms are employed. One transform linearly relates the wet normal-incident reflectivity [NI(wet)] to the hydrocarbon NI. The other transform relates the far-trace amplitude to NI for each saturation state. These transforms are derived from rock-property trends that are local to the prospect. With these two transforms and the AVO gathers at the prospect and at the down-dip water-equivalent reservoir, a test statistic can be developed that differentiates economic gas from fizz saturation. The methodology doesn’t require a calibration well that ties the seismic unless the bed thickness is desired.
A reservoir is often called “fizz” when the gas percentage in the pore space is 25 percent or less. Normally, a small amount of gas in a reservoir lowers the P-wave velocity dramatically, and then as the gas saturation increases the primary velocity does not change significantly. The porefluid prediction becomes more difficult as reservoir properties such as porosity undergo slight changes. These possible reservoir scenarios make the prediction of fizz and gas from AVO problematic as is illustrated in Figure 1 (Hilterman, 2005). Obviously, with almost identical AVO curves in Figure 1, AVO inversion, even one with three terms, will not distinguish a fizz reservoir from gas. This paper will introduce rock-property transforms that assist in the discrimination of gas from fizz. The thickness of the reservoir can also be estimated.
In Figure 2, a gas reservoir is depicted along with a downdip location where the sand reservoir is assumed to be wet. The migrated CDP gathers associated with the gas and wet locations are shown beneath the section. AVO synthetics generated by assuming thin-bed reservoirs are shown. The seismic wavelet was estimated by dynamically varying the frequency band and phase constant. In this area, it was anticipated that variations of the reservoir properties would change the hydrocarbon response and this is illustrated in Figure 3 where the velocity and density of the encasing shale properties were slightly increased. The synthetics based on the original rock properties are shown on the left for three saturations. With increased shale properties, a new lithology model is introduced and the synthetics for this new model are shown on the right. We find that the fizz-saturated reservoir for the new lithology model matches the gas-saturated synthetic for the known reservoir. This result is also depicted in Figure 1. However, the clue for discrimination lies in the fact that the wet synthetics for the two models in Figure 3 are different. If the down-dip water-saturated AVO response is available, then it might be possible to distinguish fizz from gas. This possibility initiated a rock-property analysis to determine the expected AVO variations from the original well location.
The rock-property variations in the area were estimated using histogram trend analyses generated by Geophysical Development Corporation (Hilterman, 2001).
