Efficient Detection of Productive Intervals in Oil and Gas Reservoirs

Abstract The utility and evaluation of cutoff values for net pay or netto- gross determination have been hotly debated topics since the 1950's. There are numerous subtleties to cutoffs but exactly how the values are calculated has largely been overlooked. Most cutoff users have been content to use a regression line to calculate the cutoff value. We show that cutoffs obtained using a regression line are likely to be inferior to estimates produced by other methods. When four methods were applied to two field datasets and compared, regression-based porosity cutoffs were between 1 and 2 pu different than the values which give the smallest number of errors. Monte Carlo simulations broadly support the results obtained from the datasets. One method, the ‘trial and- error’ method, performed well through most of the tests, reducing errors by 40% from those obtained using the regression line-based cutoff. All cutoff estimation methods have errors, caused by the imperfect relationships we have between variables such as porosity and permeability. This study shows we have a choice of methods. Since the better method can be easily applied in spreadsheet software, it should be a valuable addition to the petrophysicist's toolbox. Introduction Net pay (NP) may be defined as any interval containing economically producible hydrocarbon using a specific production method. It thus represents the portion of the reservoir that contains sufficient porosity, permeability, and hydrocarbons for economic exploitation. NP can be interpreted as an effective thickness that is pertinent to identification of flow units and target intervals for well completions and stimulation programs (Worthington and Cosentino, 2005). The associated net-to-gross ratio (NGR) corresponds to the proportion of the total or gross thickness which is composed of net pay. Numerous papers have reviewed and proposed methods for NP and NRG determination. Snyder (1971) covers many of the methods in use up to the early 1970's, which used the SP or GR logs and core analysis. More recent proposals include using capillary pressure (Vavra et al., 1992) and probe permeameter measurements (Fl?lo et al., 2000), and percolation modelling (McLennan et al., 2005). Of the large variety of possible methods, one approach is much more commonly discussed than any other. This method involves defining threshold values (or cut-offs) for the characteristics of interest and their surrogates. These limiting values are designed to define those rock intervals that show potential to contribute significantly to economic hydrocarbon production. The establishment of the cut-off values varies according to the data available, intended application, and economic environment. Permeability (k) is often a central parameter defining NP and NGR (Holditch et al., 1991). Unfortunately, there is no ‘continuous’ subsurface permeability measurement and core data are unlikely to be available with 100% coverage for every well. Available core data are typically used to identify plausible surrogate variables which can be measured with well logs to evaluate NP. Porosity φ is a very common choice; in the literature survey conducted by Worthington and Cosentino (2005), 23 of the 31 reports applied a porosity cutoff to evaluate NP.

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