PfEFFER Concepts


Rock Compositional Analysis


Rock Compositional Analysis

The mineralogy of reservoir zones is often tied closely to permeability and productivity in complex lithology reservoirs where differences in petrofacies mirror lithofacies changes. A variety of logs can now be used to characterize mineralogy either in graphic crossplots or by direct computation. A particularly powerful combination for this purpose is provided by the photoelectric index, neutron and density porosity logs. In this first section we will describe their application in the RHOmaa-UMaa crossplot for mineral identification and lithofacies recognition and the further use of RHOmaa and Umaa in the generation of a compositional profile. In the following section, the theory and practice of general compositional solutions are outlined that use input logs and components as specified by the user.

The RHOmaa - Umaa crossplot and composition profile

The interpretation of simple minerals and lithologies is generally straightforward from a visual inspection of the photoelectric index curve with the neutron/ density overlay. In cases of mixed lithologies or mysterious minerals, a crossplot of a zone log responses can be helpful both for purposes of identification and for semi-quantitative estimations of volumetric proportions.

The RHOmaa - Umaa crossplot was introduced to utilize measurements of the photoelectric index, neutron porosity, and bulk density for matrix mineral evaluation. The two dimensions of the crossplot require the three log variables to be condensed in some manner. This is done with a similar approach to the M-N plot methodology, through the suppression of porosity as a variable and calculation of the apparent properties of the rock matrix. The log variables that are needed for the calculation are shown diagramatically in Figure 22 .

Now, because:

then:

f is the true porosity and must be estimated by calculation or crossplot. The grain density is therefore estimated and is:

The photoelectric absorption index (Pe) is measured in units of barns per electron. In order to linearize its relation with composition, the variable must be converted to a volumetric photoelectric absorption index (U) with units of barns per cc by:

and approximated by:

Figure 22 : Source of log measurements for estimation of RHOmaa (apparent grain density) and Umaa (apparent volumetric matrix photoelectric absorption).

This is the volumetric photoelectric absorption coefficient of the zone (matrix plus fluid). The hypothetical volumetric photoelectric absorption coefficient of the matrix is UMAA. By analogy with the derivation of RHOMAA:

Because the volumetric photoelectric absorption index of water is very small, the formula is sometimes approximated by:

The values can then be located on a RHOMAA-UMAA crossplot (Figure 23) for the solution of compositions within calcite-dolomite-quartz mixtures, as well as the recognition of evaporites, clay minerals, and other components.

The RHOMAA and UMAA values used to locate zones on the crossplot may also be used to compute rock composition as the proportions of three minerals. The quartz-calcite-dolomite triangle on the RHOMAA-UMAA crossplot of Figure 23 is a graphic presentation of a common (but not unique) three mineral system that could be used. Any zone within the triangle can be resolved as proportions of the three end-members by a graphic scaled subdivision of the triangle. Alternative composition systems can be proposed as dictated by the geology of the reservoir, such as calcite-dolomite-anhydrite. In each case, a unique solution is possible provided that three components are chosen to make a determined system with the two input variables of RHOMAA and UMAA. The two inputs are sufficient, because the component proportions collectively form a closed system.

Figure 23: Framework of RHOmaa - Umaa crossplot marked with some common reference minerals and a quartz-calcite-dolomite composition triangle.

Alternatively, the composition of any zone may be solved by algebra once the mineral components are specified and presented as a compositional profile scaled to depth. These operations are easily coded in computer software as described in the following section. When used to solve compositions in a succession of zones, the results may be shown as a continuous compositional profile with depth (Figure 24).

Figure 24: RHOmaa-Umaa plot, compositional computations, and compositional
profile of dolomite, quartz, and calcite in a Krider Limestone(Permian Chase Group) section from south-west Kansas.

This page updated July 2010
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