Abstract: Seismic Lithofacies Prediction Using AVO-Analysis: Application to a North Sea Deep-Water Clastic System

Avseth, Per; Gary Mavko and Tapan Mukerji - Stanford University

Deep-water clastic systems can show very complex sand distributions, and reservoir description based on conventional seismic and well-log stratigraphic analysis may be very uncertain in these depositional environments. We present a methodology for predicting lithofacies from seismic amplitudes, and apply it to a North Sea deep-water clastic system of Paleocene age which includes an oil field of economic interest. Lithofacies have a major control on reservoir geometries and porosity distributions. Therefore, the innovation in this methodology is to link lithofacies to seismic properties thus improving our ability to use seismic amplitude information in the reservoir characterization of deep-water clastic systems.

We first define seismic lithofacies as seismic scale sedimentary units with characteristic lithology, bedding configuration, petrography and seismic properties. We classify lithofacies for 7 wells in the area based on sonic, gamma ray and density logs, and create cumulative distribution functions (cdfs) of seismic properties for each facies. Pore fluid variations are accounted for by applying Biot-Gassmann theory. The well log derived cdfs show that unconsolidated thick-bedded clean sands with water, plane laminated thick-bedded sands with oil, and pure shales have very similar acoustic impedance distributions. However, the Vp/Vs-ratio resolves these ambiguities. We therefore conduct AVO (amplitude versus offset) analysis to predict seismic lithofacies from seismic data. We assess uncertainties in AVO response related to the inherent natural variability of each seismic lithofacies using a Monte-Carlo technique. AVO probability plots show that there are overlaps between different facies, but the most likely responses for each facies are nicely separated. Zero-offset reflectivity (R(0)) versus AVO-gradient (G) bivariate probability plots are created to better assess the overlap between the different facies (Fig. 1). We then analyze real CDP-gathers at several well locations, and successfully predict the seismic lithofacies indicated by the well log data. This demonstrates the feasibility of AVO-analysis to predict seismic lithofacies. Figure 2 shows a seismic stack section, where we have extracted R(0) and G for the picked horizon, representing the top of the studied system. Combining the observed R(0) and G values with the bivariate probability distributions, we predict a transition from water-saturated marginal interbedded sand-shale units to oil-saturated thick-bedded sands in the center of a lobe-channel. By analyzing selected near- and far-offset seismic stack sections, AVO-attribute sections and seismic amplitude horizon maps, we show that the studied deep-water clastic system is a point-sourced sub-marine fan in which cemented clean sands are indicated in the feeder-channel, unconsolidated clean sands in the lobe-channels, and interbedded sand-shale facies and shaly sands in interchannel and marginal areas of the system. In general, this study shows how geophysical and statistical methods can be integrated to predict the reservoir characteristics of deep-water clastic systems from seismic data.

AAPG Search and Discovery Article #90933©1998 ABGP/AAPG International Conference and Exhibition, Rio de Janeiro, Brazil