Improved Quantification of Fit-for-Purpose Saturation Exponents

Abstract

Summary Saturation exponent is a key input parameter to those petrophysical algorithms that are used for the evaluation of water saturation Sw. A longstanding problem is that different interpretative algorithms often deliver different predictions of Sw in the same reservoir rock, especially where the reservoir shows shale effects. This problem is overcome by determining a fit-for-purpose saturation exponent, which is specific not only to a given core sample but also to a particular interpretative algorithm for the evaluation of Sw.By using fit-for-purpose saturation exponents and published type algorithms for the petrophysical evaluation of Sw, it is shown that the discrepancies between predicted water saturations obtained using different interpretative equations can be confined to a highly restricted range. Through this process, the sensitivity associated with the choice of a petrophysical model for the evaluation of Sw is demonstrably contained. This concept is extended to take account of the degree of maturity of the core database. Four expanding data scenarios are enacted to derive data-specific values of the fit-for-purpose saturation exponent using the best-performing type algorithms. The first scenario assumes no core data beyond measurements of Sw and resistivity index using simulated formation water; results are erratic. Scenario 2 draws additionally on a measurement of porosity for each desaturated core plug; it sets the intrinsic porosity exponent m* = 2. Values of Sw predicted using saturation exponents established through Scenario 2 compare closely with those evaluations generated through more-data-intensive scenarios. Therefore, Scenario 2 is proposed as the most efficient way of deriving a fit-for-purpose saturation exponent and thence of obtaining a meaningful evaluation of Sw. A specific type algorithm has been identified for doing this most effectively. The method has been validated against benchmarks from a public database. The predictive performance of the preferred type algorithm for evaluating Sw through Scenario 2 matches even a fully comprehensive shaly-sand approach with all its additional characterizing data. Therefore, the use of fit-for-purpose saturation exponents allows data needs to be optimized alongside the further containment of uncertainty in integrated reservoir description. Thus, both costs and risk can be reduced simultaneously.