An integrated system of acquiring data from key wells to calibrate petrophysical models has proven effective in formation evaluation programs. In the key wells, extensive logging, coring and fluid sampling programs provide the data used to develop better predictive models for water saturation, lithology, porosity, and permeability. These enhanced models have reduced uncertainties and resulted in more accurate hydrocarbon-in-place calculations.

Case studies show how the drilling fluid, coring and logging programs are designed to provide optimal conditions for obtaining good core and logs. In oil fields, key wells are cored with oil-base-mud. The cores are carefully preserved in the field, CT scanned and then sampled for geological and petrophysical analyses. Water saturation is calculated by different methods: Dean-Stark extraction from the oil-base-mud core; log analysis using the cementation (m) and saturation (n) exponents from the laboratory program; and saturation height functions derived with capillary pressure data. Using all these sources, it is also possible to back-calculate the optimum “m” and “n” values for future log analysis in the field.

Facies prediction models have facilitated development of other facies-based petrophysical models. Thin section petrography, X-ray diffraction, scanning electron microscopy, and pore throat size distribution from mercury injection capillary pressure data are used to determine rock types, from which samples are selected for special core analysis. Robust artificial neural networks are used to develop models to predict permeability in uncored wells. Laboratory NMR measurements on core samples provide T2 cutoffs for bound and free fluids. These and core permeability data facilitate the determination of the scaling factors in the Timur-Coates equation to calculate permeability from NMR logs.

This strategy of using data from key wells has enhanced formation evaluation programs. Significant benefits include reduced uncertainty in determining hydrocarbon pore volumes, improved integration of geological and engineering data and enhanced static and dynamic reservoir models.