Petrophysical rock classification in the Cotton Valley tight-gas sandstone reservoir with a clustering pore-system orthogonality matrix

Abstract

Petrophysical rock classification is an important component of the interpretation of core data and well logs acquired in complex reservoirs. Tight-gas sandstones exhibit large variability in all petrophysical properties due to complex pore topology resulting from diagenesis. Conventional methods that rely dominantly on hydraulic radius to classify and rank reservoir rocks are prone to rock misclassification at the low-porosity and lowpermeability end of the spectrum. We introduce a bimodal Gaussian density function to quantify complex pore systems in terms of pore volume, major pore-throat radii, and pore-throat radius uniformity. We define petrophysical dissimilarity (referred to as orthogonality) between two different pore systems by invoking the classic "bundle of capillary tubes" model and subsequently classify rocks by clustering an orthogonality matrix constructed with all available mercury injection capillary pressure data. The new method combines several rock textural attributes including porosity, pore-throat radius, and tortuosity for ranking reservoir rock quality in terms of flow capacity. We verify the new rock classification method with field data acquired in the Cotton Valley tight-gas sandstone reservoir located in the East Texas basin. The field case shows that the new method consistently identifies and ranks rock classes in various petrophysical data domains, including porositypermeability trends, pore-size distribution, mercury injection capillary pressure, and NMR transverse relaxation time (T 2) spectra. Relative permeability curves, which are difficult to measure in the laboratory for tight rocks, are quantified with Corey-Burdine's model using the bimodal Gaussian pore-size distribution and are validated with core data.

Classification of Tight Sandstone Reservoirs Based on the Nuclear Magnetic Resonance T2 Distribution: A Case Study on the Shaximiao Formation in Central Sichuan, China

“…The Gaussian distribution function is used to fit the NMR T 2 distribution. The probability density function (PDF) of the Gaussian distribution is expressed as − where T 2 is the NMR transverse relaxation time expressed in ms, ∑ i = 1 n ω i = 1, 0 < ω i < 1 ( i = 1– n ), and ω i , μ i , and σ i are the i th weight coefficient, mean, and standard deviation, respectively.…”

Rock Typing of Tight Gas Sands: A Case Study in Lance and Mesaverde Formations From Jonah Field

“…Starting from Archie's (1950) pioneering work on petrophysical characterization of rocks based on pore size distribution, rock classification is extended to hydraulic flow units (Pittman, 1992), flow zone indicator (FZI) (Amaefule et al, 1993;Prasad, 2003) and pore-system orthogonality matrix methods (Xu and Torres-Verdín, 2014). Archie (1950) describes pore geometry as the central part of different properties such as porosity, permeability, saturation height, capillary pressure.…”

“…However, sensitivity decreases when an individual porosity-permeability correlation is defined, because the entire range of porosity is restricted and fails to characterize rocks with single FZI unit (Castillo et al, 2012). Xu and Torres-Verdín (2014) use forward modeling and inversion to extract petrophysical attributes such as pore throat radius, standard deviation and mean of pore throats in order to group rocks in a pore-system orthogonality matrix. The reliability of this method depends on optimization of bimodal Gaussian density function.…”

Petro-Elastic Log-Facies Classification Using the Expectation–Maximization Algorithm and Hidden Markov Models

“…The Gaussian distribution belongs to a family of distributions that enable all parts of the Baum-Welch algorithm to be analytically tractable (Baum et al 1970). Gaussian mixture models have been previously used in a facies classification context in Grana and Della Rossa (2010) and Xu and Torres-Verdin (2014). Differently from these applications, the current approach aims to estimate the unknown parameters of the mixture, as well as the unknown parameters of the vertical sequence transitions through the EM method.…”