Predicting Lithology And Reservoir Quality From Acoustic Velocities Based On Petrophysical Classification of Siliciclastics

Based on the recently developed petrophysical classification of siliciclastics, which takes into account amount C and textural position of clay, it is shown that acoustic velocities can be a fairly accurate tool in predicting lithology, porosity, and permeability of these rocks. Four major petrophysical groups of carbonateand organic-poor siliciclastics are distinguished: 1) clean arenites (C 35%). Compressional velocity versus porosity relation for consolidated rocks in each of these groups is found to be linear with very high correlation coefficients. This allows for remarkably accurate porosity estimates or lithology prediction in consolidated siliciclastics from acoustic velocities compared to the widely used time average (Wyllie) equation, its improved modification (Raymer equations), both of which neglect textural factors. The processes of incipient consolidation of clastic sediments are characterized by a steeper slope of the velocity-porosity transform due to a more pronounced velocity increase compared to the porosity reduction at this stage. Finally, the potential for improved permeability prediction via porosity, which can be estimated from from the velocity data and lithology, is shown. I N T R O D U C T I O N Numerous laboratory and field experiments suggest that relatively simple relations exist between seismic velocities and such important rock parameters as porosity and clay content (Wyllie et al., 1956; Raymer et al., 1980; Tosaya and Nur, 1982; Kowallis et al., 1984; Han et al., 1986). These relations, however, largely neglect lithological parameters such as mineralogy, textural position of clay, and pore size and shape distribution, which are most closely linked to the original composition, depositional environment, and diagenesis of clastic sedimentary rocks and are of great and growing importance in oil exploration and reservoir characterization. It seems logical that in order to gain a better physical insight into the relations among elastic properties, porosity, and permeability in such complex heterogeneous systems as sedimentary rocks, the latter must first be classified according to some principles that consider both petrophysical and compositional and textural features. In this paper, based on the petrophysical classification of siliciclastics (Vernik and Nur, 1992), which incorporates petrography and rock physics of this major class of lithologies, and utilizing an extensive compilation of previously reported laboratory ultrasonic velocity measurements in water saturated siliciclastics under the effective pressure of 40 MPa, I propose new empirical compressional velocity vs. porosity relations. The latter can be utilized either for a more detailed lithology identification in siliciclastic rock formations or for porosity prediction from acoustic velocities in those formations. Further, it is shown that the total porosity can be used as an intermediate parameter to link acoustic velocity and permeability within distinct petrophysical groups. PETROPHYSICAL CLASSIFICATION OF SILICICLASTICS Over the years since its first introduction, the sonic log, let alone VSP and cross-hole seismic, has always remained a secondary in significance tool in lithology or porosity prediction and formation evaluation primarily because of a considerable scatter of the experimental data points in the velocity vs. porosity space. A certain value of compressional velocity Vp at a certain effective stress level in siliciclastic rocks generally corresponds to a wide range of about 15 porosity units. As suggested by Tosaya and Nur (1982), Kowallis et al. (1984), and Han (1986), the porosity prediction can be greatly improved when using empirically derived multivariate linear regression equations including the clay content as an independent variable. However, these equations are of limited use because they require that the clay content be accurately estimated which of course is very hard to do in practice. Petrophysical classification of carbonateand organicpoor siliciclastics proposed by Vernik and Nur (1992) based on the study of the diverse experimental database of 104 carbonateand organic-poor, clastic sedimentary rocks and sediments (compiled from Han, 1986; Klimentos and McCann, 1990; Domenico, 1977; Strandenes and Blangy, 1991; Vernik and Nur, 1992) allows a qualitatively new approach to the problem of porosity prediction from acoustic velocities. According to the classification, sandstones bearing less than 15% vol. clay matrix are referred to as arenites with further subdivision according to grain mineralogy into clean (normally quartz) arenites (less than 2% clay) and arenites proper. Sandstones with 15% or more matrix are referred to as wackes with further transition at 35% clay to shales. It is particularly important to emphasize the textural difference between the clean arenites/arenites, which, based on thin section observations, are predominantly grainsupported rocks, and wackes/shales that are clay matrixsupported. Therefore the threshold (i.e., critical) intergranular porosity in the vast majority of consolidated, clay-bearing sandstones is in fact somewhere around 15% rather than 3540% as suggested by the physical modelling studies on sands (e.g., Marion, 1990). Load-bearing structure of clastic sediments can be alternatively estimated from the variations in microporosity of clay matrix with diagenesis. By measuring the total (helium) porosity macroporosity and volumetric clay content C (both in thin sections) one can evaluate = with an adequate accuracy. The analysis of the extensive database available at Stanford Rock Physics Project performed this way yielded = 65±14% for arenites (C < 15%) and, as expected, a broad range of values from 15% to 64% averaging 36% for wackes with higher values in shallow, poorly consolidated rocks and lower values in tight gas sandstones. NEW VELOCITY TRANSFORMS AND LITHOLOGY PREDICTION IN CONSOLIDATED SILICICLASTICS The Vp versus porosity plot for consolidated siliciclastics is shown in Figure 1. The data set was preliminary differentiated according to the petrophysical classification discussed above. The four best fit linear regressions corresponding respectively to the four petrophysical rock groups shown in Figure 1 are characterized by a good spatial resolution and very high correlation coefficients R = 0.96-0.99. In Figure 2 I plotted a subset of the experimental data that incorporates only consolidated clean arcnites (2a) and arenites (2b) together with