AVO attribute analysis and seismic reservoir characterization

Abstract

This article summarizes some basic concepts in AVO processing and the computation of prestack seismic attributes. Seismic modelling forms the basis for understanding the seismic signature. It helps in the prediction of reservoir characteristics away from well control points. Reliable estimation of petrophysical parameters is needed as input for such studies. These petrophysical estimates are an integral part of more advanced reservoir characterization and modelling. First, the AVO principles are described and various prestack attributes are presented. Subsequently, the elastic approach is discussed and finally the benefits of seismic modelling with advantages of multi-disciplinary reservoir studies are demonstrated. The amplitude character of seismic reflections varies with offset, due to changes in the angle of incidence. The commondepth- point (CDP) gather (Fig. 1a) shows the variation for different traces. Figure 1b illustrates the changes in the seismic response when a water-wet brine-filled reservoir is replaced by oil or gas. The synthetics are calculated along a normal incidence and zero offset trajectory. The hydrocarbon saturation is set at 80% (Robinson et al., 2005). Both hydrocarbon cases show brightening of the reflection with respect to the brinefilled scenario. The sands have lower acoustic impedance (AI) than the encasing shales. Not only the top reservoir reflection shows this increased contrast tendency, but the seismic loop directly below it also manifests considerable changes. Figure 2 illustrates a positive gas-sand reflection decreasing with offset, while the negative water-wet reservoir above shows less reflectivity change. The polarity of the data is normal, i.e. an increase in acoustic impedance with depth (or hard kick) corresponds to a peak to the right on the seismic traces. The two highlighted reservoirs are of differing petrophysical character and the encasing geology (compaction/lithology) changes with depth. Although this kind of amplitude variation is evident on the prestack CMP gathers, it has been somewhat ignored in the past by interpreters because they work primarily with the stacked migration data set. Nowadays, special studies are conducted on a routine basis to analyse the behaviour of the ‘amplitude-versus-offset’ (AVO-studies). This type of data contains detailed information on the porefill of reservoirs (e.g. Ostrander 1984; Castagna and Backus 1993; Chiburis et al., 1993; Hilterman 2001; Veeken et al., 2002; Da Silva et al., 2004a). Ultimately it will lead to a more efficient evacuation of hydrocarbons with substantially improved recovery factors (Fig. 3). The amplitude behaviour of the different raypaths also varies according to the porefill and lithology. Water-filled reservoirs often show variations in amplitude with offset that are different from those of hydrocarbon-filled reservoirs. The change in zero-offset reflectivity R0, or intercept, is the most diagnostic feature. The seismic response depends on the encasing geology, porefill, and interference effects. It varies with depth and differs in various parts of the world. Studying the prestack differences in detail can indicate the causes of near- and far-offset amplitude variability (Fig. 4). The seismic signature from a gas sand is different from the brine-filled response when the same reservoir is observed under similar conditions. In such a situation, the encasing geology is probably the same and has little influence on the observed anomalous amplitude behaviour. A distinct change in zero-offset reflectivity is probably the most remarkable phenomenon. Changes in amplitude with offset can occur in hydrocarbon- as well as water-bearing reservoirs; in that case the intercept might contain the vital porefill information. The AVO effect represents a potentially powerful tool to discriminate between water- and hydrocarbon-saturated reservoirs. However, it means going back to the prestack domain. It should be ensured that the data on individual CDP gathers come from a consistent subsurface location. This is generally achieved by a proper migration of the input data set (prestack time migration, Da Silva et al., 2004b). Careful data preconditioning is essential when quantitative interpretation is the ultimate aim (Veeken and Da Silva, 2004).