Modeling Offset-Dependent Reflectivity for Time-Lapse Monitoring of Water-Flood Production in Thin-Layered Reservoirs
Seismic time-lapse monitoring of production is an important tool used to efficiently drain a hydrocarbon reservoir. Repeat seismic surveys may be used, because the seismic method is sensitive to the reservoir fluid. A prominent seismic attribute is the reflectivity (or amplitude) as a function of offset (AVO) which strongly depends on material properties, and hence, on the pore fluid. Repeat surveys, however, are very costly. To reduce the risks, the repeat survey is simulated on a computer for a number of different scenarios. Hence, the objectives of this study are to predict the seismic responses after five years of production of the reservoirs at the well locations, correlate the seismic attributes to fluid conditions in the reservoirs, assess the detectability of changes in AVO attributes due to changes in fluid conditions, and determine which attribute is more diagnostic of fluid changes.
Petrophysical models were generated for different pore fluids using well logs from a field in the Gulf of Mexico. Synthetic seismograms were then calculated using a layerstack scheme to study the effects of the reservoir fluids on AVO. Compared to idealized half-space models, it was found that the AVO responses are contaminated by the overburden and the thinness of the reservoir. In order to remove transmission loss due to overburden effects, the synthetic AVO curves were scaled by normalizing an overburden-over-half-space model to an idealized analytical Zoeppritz model. In a second step, an offset-dependent overburden correction was applied using a low order polynomial, which was fitted to the amplitude ratios between the overburden/half-space model and the idealized model. Finally, a zero-offset tuning correction was applied.
The results of the AVO analyses showed that some errors were unresolved using the applied overburden and tuning corrections, and amplitudes at large offsets were possibly contaminated by multiples and converted waves. Since there is no shallower production or steam injection for this particular field, the repeat surveys should have the same overburden, tuning, multiple-related and converted wave contamination. It appears reasonable to assume that any changes in amplitude between the repeat surveys would be due to fluid saturation changes. Therefore, it was concluded that it is not necessary to attempt to remove the overburden and tuning effects.
Results from the AVO analyses of the uncorrected models showed that AVO attributes should be a useful tool to detect reservoir conditions during the production of the field. Generally, the water-flood changes the AVO by decreasing the intercept and increasing the gradient from the in-situ oil/gas cases. The relative changes in both intercept and gradient due to the water-flood are detectable assuming a 20% relative-change detection threshold, and gradient is more diagnostic because the relative change in gradient is very large compared to that for intercept.