Three carbonate porosity classifications are currently in widespread use in the oil and gas industry (Figure 1); Archie (1952), Choquette and Pray (1970) and Lucia (1983, 1995). The most useful in relating well-log character to reservoir quality is that of Lucia (1995, 2007). Lucia’s classification of porosity in a carbonate follows on from the work of Archie (1952) and his quantitative documentation that pore throat size distribution is critical to reservoir quality understanding. Lucia’s porosity classification is most useful when applied during the production-stage of reservoir characterization when significant volumes of rock-property data are available for study. It is more difficult to use in earlier phases of geological exploration and modelling where quantitative rock-based understanding is minimal, and poroperm geometries must be inferred using minimal data. If the data is there, I tend to use both to garner the best understanding of subsurface rock property distribution.
1. Petrophysical classifications of carbonate pore types as first defined by Archie (1952) compared with the fabric-selectivity concept of Choquette and Pray (1970) and Lucia (1983).
Choquette and Pray (1970) recognise 15 different basic porosity types, as shown in Figure 2. Each type is a physically or genetically distinct and differentiated by variations in pore size, shape, genesis, or relationship to other elements of the fabric, Carbonate porosity is described in detail by Ahr 2008, Moore 2009; see below) and listed as fabric/pore elements in Figure 1.
Most of the pore types in the Choquette and Pray classification are self-explanatory, but a few of the more esoteric types require further explanation. Growth framework porosity encompasses the primary pore space between reef formers as the biota builds a bioherm or boundstone. Fenestral porosity refers to penecontemporaneous or primary pores larger than the surrounding grain-supported fabric. Fenestral pores can develop in beach sands or carbonate muds as a result of air or gas bubbles in the sediment. Umbrella or shelter porosity describes pores preserved beneath cones disarticulated bivalve shells, for example. Neither fenestral or umbrella (shelter) pores contribute large volumes to comic levels of porosity in a reservoir. Mouldic porosity forms when a fabric element dissolves, such as a shell or a crystal cluster of a particular mineral (e.g., dolomite rhombs or anhydrite nodules).
Channel porosity refers to large somewhat irregular elongate pores cutting across rock fabric generally associated with the solution enlargement of fractures. Vug porosity in their definition refers to irregular pores cutting across rock fabric elements such as grains, cement, or crystals and generally range from cm to decimetres in diameter.
Fabric selectivity is the critical component of the Choquette and Pray (1970) classification (Figure 2). Fabric is defined as the solid depositional and diagenetic constituents of a sediment or rock. These solid constituents consist of various types of primary grains, such as ooids and bioclasts; later-formed diagenetic constituents, such as calcite and dolomite cements; and recrystallisation or replacement components, such as dolomite and calcium sulphate crystals (Figure 3). If a dependent relationship exists between porosity and particular fabric elements, that porosity is referred to as fabric-selective, which can be primary or secondary (Figure 2). If no relationship between fabric and porosity can be established, the porosity is classed as not fabric-selective. Then there’s the third grouping of textures that may or may not be fabric selective (Figure 2).
Two factors are essential for interpreting fabric selectivity: the configuration of pore boundaries and the position of pores relative to fabric (Choquette and Pray, 1970). In most primary porosity, pore boundary shapes and pore locations are determined entirely by fabric elements. Primary intergranular pore space in unconsolidated sediments, therefore, is fabric-selective because its configuration is determined solely by depositional particles. The same is true for primary intragranular porosity, which is controlled by the shape and location of cavities determined by the nature of growth of the organism that produced the particle.
In secondary pore systems, however, porosity may be either fabric-selective or not, depending primarily on diagenetic history. As an example, mouldic porosity is commonly fabric-selective because of preferential removal of particular fabric elements from the rock, such as early removal of aragonitic ooids and bioclasts during mineral stabilisation. Another example is the later removal of anhydrite, gypsum, or even calcite from a dolomite matrix during diagenesis. On the other hand, cavern and fracture development typically cut across fabric elements. In rocks without significant tectonic overprint, fracture orientation is controlled primarily by joint systems. Fracture porosity is therefore not fabric-selective and tends to link up other forms of porosity, which is why many giant oil fields in carbonates have significant levels of their effective porosity contributed by fractures.
Primary porosity refers to any porosity present in a sediment or rock at the termination of depositional processes. Secondary porosity can develop at any time after deposition, and the length of time involved may be enormous. The locus and geometry of processes creating secondary porosity generation may be divided into zones based on differences in the porosity-modifying processes occurring in shallow subsurface versus those encountered during deep burial (eogenetic versus mesogenetic; see skill set).
Proportions of porosity contributed by various elements of the classification can be quantified by the use of a ternary diagram and so define pore facies (Figure 4; Ahr, 2008). “ Pore facies ” are constructed from petrographic and stratigraphic data, as described by Kopaska - Merkel and Mann (1993) with the concept further developed by Nazemi et al. (2018). Reservoir pore types are mapped based on their depositional and diagenetic characteristics so that pore facies maps can be constructed to pinpoint the spatial distribution of the pore types with highest corresponding permeability and lowest resistance to ﬂuid ﬂow and that can be correlated stratigraphically at the ﬁeld scale.
Pore facies, as outlined in Figure 4, encompass particular characteristics such as ﬂuid-ﬂow, pore-throat size distributions and reservoir properties for various reservoir rocks (Nazemi et al., 2018). Generally, pore facies encompass several pore types, but they also may comprise only a single pore type (Bahrami et al., 2017). Identiﬁed pore facies were categorised into three main groups in the units studied in Pars Field, offshore Iran (Nazemi et al., 2018). The dominant three clusters are; PF1-dominated by primary or sedimentary pores (interparticle, intraparticle and fenestral); F2-fabric selective pores (mouldic and intercrystalline formed by fabric retentive dolomitization); and PF3- non-fabric selective pores (vuggy, cavernous, fracture, channel, and intercrystalline), with pore facies4 > pore facies2 > pore facies 1).
2. Porosity classification of Choquette and Pray (1970
3. Some grain-scale porosity examples, according to Choquette and Pray (1970). A) Entrada Fm (Jurassic) mature quartz arenite with interparticle porosity. B) Entrada Fm (Jurassic) mature quartz arenite, Polyhedral (intercrystal) porosity due to quartz overgrowth cement. C) Tansill Fm (Permian) green alga (Mizzia) with intraparticle porosity D) Lower Coralline Lst Fm (Oligocene) with intraparticle porosity in large benthic foram shells. E) Skinner Ranch Fm (Permian) dolomite with polyhedral intercrystalline porosity. FZ) Ronda unit (Jurassic) with crystal-mouldic porosity (black) due to the dissolution of dolomite rhombs (images courtesy of Scholle et al. (2003) and Ulmer-Scholle et al., (2014)).
In PF1 in Pars Field depositional porosities form up to up to 70% of the measured pore fabric and so is called depositional pore facies. In this group, dominant pores include interparticle, intraparticle and fenestral, all of which are less aﬀected by diagenetic processes. Pore spaces in pore facies 2 are made up of 30–70%combined depositional and fabric selective diagenetic pores. Indeed, this pore facies includes petrophysical attributes of both pore facies 1 and 4. Pore facies 3 is composed of PF1 and PF6, which is a mixture of pore facies analogous to pore facies 2. In this group, between 30 and 70% of pores are non-fabric selective or depositional pore types. PF 4 volumetrically is composed of more than 70% of diagenetic fabric-selective pores (mouldic and fabric-retentive intercrystalline), and so is called the fabric selective pore facies. In PF 5, about 70% of pores have a diagenetic origin and fabric-selective, and non-fabric-selective pores form about 30–70% of this pore facies. This PF defines a mixture of PF4 and PF6 properties. PF 6 is called non-fabric-selective pore group as over 70% of its pores are non-fabric-selective. This pore facies includes vuggy, cavernous, fracture, stylolitic, and fabric-destructive intercrystalline pore type.
Using a pore facies understanding, each episode of diagenesis leaves distinctive traces that can be compared with other microscopic traces to reveal the timing of each event. This tracing of diagenetic events is done by identifying cross-cutting relationships in core, thin sections and Image logs. The latest or last event cuts across the previous one, and so on, until the ﬁrst diagenetic event can be isolated. Pore facies can then be sorted using their capillary pressure curve characteristics and so obtain quantified estimates of reservoir recovery efficiency and heterogeneity to select or grade candidate ﬁelds, sectors of ﬁelds, or reservoir zones for enhanced and improved oil recovery (Figure 5; Ahr 2008).
In this way, episodes of dissolution, cementation, compaction, or other forms of diagenesis as defined by Choquette and Pray (1970)can be identiﬁed and placed in chronological and geometric sequence to reveal the burial history and the geological cause-effect system that modiﬁed the reservoir rocks (Ahr, 2008).
Ahr, W. M., 2008, Geology of carbonate reservoirs: the identification, description, and characterization of hydrocarbon reservoirs in carbonate rocks: Hoboken, N.J., Wiley InterScience, 277 p.
Archie, G. E., 1952, Classiﬁcation of carbonate reservoir rocks and petrophysical considervations: Bulletin American Association Petroleum Geologists, v. 36, p. 278-298.
Bahrami, F., R. Moussavi-Harami, M. Khanehbad, M. H. M. Gharaie, and R. Sadeghi, 2017, Identification of pore types and pore facies for evaluating the diagenetic performance on reservoir quality: a case study from the Asmari Formation in Ramin Oil Field, SW Iran: Geosciences Journal, v. 21, p. 565-577.
Choquette, P. W., and L. C. Pray, 1970, Geologic Nomenclature and Classification of Porosity in Sedimentary Carbonates: Bulletin American Association Petroleum Geologists, v. 54, p. 207-250.
Kopaska-Merkel, D. C., and S. D. Mann, 1993, Classification of Lithified Carbonates Using Ternary Plots of Pore Facies: Examples from the Jurassic Smackover Formation, in R. Rezak, and D. L. Lavoie, eds., Carbonate Microfabrics: New York, NY, Springer New York, p. 265-277.
Lucia, F. J., 1983, Petrophysical parameters estimated from visual description of carbonate rocks: a field classification of carbonate pore space: Journal of Petroleum Technology, March, v. 35, p. 626-637.
Lucia, F. J., 1995, Rock-fabric/petrophysical classification of carbonate pore space for reservoir characterization: American Association of Petroleum Geologists Bulletin, v. 79, p. 1275-1300.
Lucia, F. J., 2007, Carbonate Reservoir Characterization; An Integrated Approach (Second Edition), Springer Berlin Heidelberg, 336 p.
Nazemi, M., V. Tavakoli, H. Rahimpour-Bonab, M. Hosseini, and M. Sharifi-Yazdi, 2018, The effect of carbonate reservoir heterogeneity on Archie's exponents (a and m), an example from Kangan and Dalan gas formations in the central Persian Gulf: Journal of Natural Gas Science and Engineering, v. 59, p. 297-308.
Scholle, P. A., and D. S. Ulmer-Scholle, 2003, A Color Guide to the Petrography of Carbonate Rocks: Grains, textures, porosity, diagenesis; Tulsa, Okla, American Association of Petroleum Geologists Memoir 77, 459 p.
Ulmer-Scholle, D. S., P. A. Scholle, J. Schieber, and R. J. Raine, 2014, A Color Guide to the Petrography of Sandstones, Siltstones, Shales and Associated Rocks: AAPG Memoir 109. 493 p.
At Saltworks, the aim of all our training modules and workshops is two-fold. 1) give an understanding of the relevant process, 2) train participants in the application of the skill sets tied to the concept and prioritise the skill sets needed to apply this understanding. Below we illustrate the skills and knowledge necessary to recognise subsurface evaporite salts using a conventional suite of well logs.
If you want to know more, please download the relevant saline geosystems or carbonate geosystems catalogue and choose a combination of the various training modules that best suite your company needs.