A formation evaluation technique is being used to evaluate the Delaware sandstones of the Permian Basin for hydrocarbons. The technique consists of crossplotting the neutron porosity and the shear wave travel time. The sandstones reservoirs that are productive will exhibit a greater shear wavetravel time than would be predicted from the neutron porosity. This technique is applicable to both open and cased hole conditions.


The sandstone reservoirs of the Permian Delaware Mountain Group of the Permian Basin are often difficult to evaluate for hydrocarbon production. The combination of high irreducible water saturations, complex mineral assemblages, porosity and permeability variations all contribute to masking productive intervals from nonproductive intervals. Add to these variables the daunting task of evaluating intervals in the cased hole with its myriad of complexities and the challenge become more intense. However, the potential production offsets the inherent risks when a consistent method can be applied to the evaluation of these reservoirs.

One successful evaluation technique is to crossplot the response of the neutron porosity and the shear wave travel time. The cross plot is then used to define the matrix parameters or scales to be used on a standard log versus depth presentation.

Figure 1 shows a crossplot chart relating shear wave travel time, or shear slowness, to porosity values. Assuming a pure mineral matrix, one can then determine a porosity value using a shear wave travel time and this chart. However, the sandstone reservoirs of the Delaware Mountain Group will range in mineral composition from quartz dominant rocks to "sandstones" with equal quantities of quartz, potassium feldspars, significant quantities of dolomite and various clay minerals. These various assemblage defy an approach using standard crossplot charts. To reflect the changes in mineral composition and porosity values a crossplot chart is devised to reflect the varying matrix travel times. In order to evaluate a zone of interest, a least-squaresregression analysis is run on the data set.

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