Rock Classification in Carbonate Reservoirs Based on Static and Dynamic Petrophysical Properties Estimated from Conventional Well Logs

Abstract

Rock typing in carbonate reservoirs is challenging due to high spatial heterogeneity and complex pore structure. In extreme cases, conventional rock typing methods such as Leverett's J-function, Winland's R 35 , and flow zone indicator are inadequate to capture the heterogeneity and complexity of carbonate petrofacies. Furthermore, these methods are based on core measurements, hence are not applicable to uncored reservoir zones. This paper introduces a new method for petrophysical rock classification in carbonate reservoirs that honors multiple well logs and emphasizes the signature of mud-filtrate invasion. The method classifies rocks based on both static and dynamic petrophysical properties. An inversion-based algorithm is implemented to simultaneously estimate mineralogy, porosity, and water saturation from well logs. We numerically simulate the process of mud-filtrate invasion in each rock type and quantify the corresponding effects on nuclear and resistivity measurements to derive invasion-induced well-log attributes, which are subsequently integrated into the rock classification. Under favorable conditions, the interpretation method advanced in this paper can distinguish bimodal from uni-modal behavior in saturation-dependent capillary pressure otherwise only possible with special core analysis. We successfully apply the new method to a mixed clastic-carbonate sequence in the Hugoton gas field, Kansas. Rock types derived with the new method are in good agreement with lithofacies described from core samples. The distribution of permeability and saturation estimated from well-log-derived rock types agrees with routine core measurements, with the corresponding uncertainty significantly reduced when compared to results obtained with conventional porosity-permeability correlations.

Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms

“…Integration of rock facies classification into the formation permeability modeling, especially given the core measurement and well log interpretations, is a crucial step to reduce the uncertainty in reservoir characterization (Xu et al 2012). Rock facies classification leads to improve the relationship between permeability and porosity and then results in efficiently estimating the petrophysical properties in noncored intervals (Lee and Datta-Gupta 1999).…”

Fundamental controls on fluid flow in carbonates: current workflows to emerging technologies

“…A wide range of rock-typing methods has been proposed for carbonate reservoirs (e.g. Lucia 1999;Gomes et al 2008;Hollis et al 2010;Xu et al 2012;Kazemi et al 2012;van der Land et al 2013;Chandra et al 2014;Skalinski & Kenter 2014). However, these methods are commonly more oriented towards practical industry workflows, such as defining empirical porositypermeability transforms that translate porosities measured at the wireline scale to permeability values in reservoir models.…”

“…A number of workflows exist that aim to define rock types with unique hydraulic properties for carbonate reservoirs (e.g. Lucia 1999; Gomes et al 2008;Hollis et al 2010;Xu et al 2012;Kazemi et al 2012). However, as mentioned before, these workflows are not based on first principles.…”

Inversion-based petrophysical interpretation of logging-while-drilling nuclear and resistivity measurements

“…Additionally, the well includes routine core measurements. Due to low reservoir pressure, deep mud-filtrate invasion affects the nuclear logs and even the deep resistivity log, such that log-derived S hc is considerably lower than in-situ S hc for water-base mud (Xu et al, 2012). Table 5.3…”

“…This behavior can be attributed to variations in Archie's parameters for differing rock types along the well. Furthermore, S w in core samples could increase due to quick spurt loss in low porosity, low pressure reservoirs (Xu et al, 2012).…”