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Introduction
The term petrofacies is defined in the sedimentary literature with different meanings. The dominant body of published work defines petrofacies solely in terms of the main detrital composition of sandstones and conglomerates, related to sedimentary provenance patterns (eg, Stanley, 1976; Gandolfi et al, 1983; Ingersoll, 1990 ; Large and Ingersoll, 1997). ; Trop and Ridgway, 1997; Critelli and Nilsen, 2000; Hendrix, 2000; Michaelsen and Henderson, 2000; Savoy et al., 2000; Dickinson and Lawton, 2001; Marenssi et al., 2002). A few studies refer to petrofacies as the main petrographic features of carbonate, evaporitic, or muddy rocks (eg, Kopaska-Merkel and Friedman, 1989; Kulick and Theuerjahr, 1989; Ching and Friedman, 2000; Testa and Lugli , 2000). Even fewer studies define petrofacies solely in terms of petrophysical and log characteristics, entirely independent of petrographic characterization (eg, Watney et al., 1999; Bhattacharya et al., 2005). Our goal here is to redefine petrofacies as a concept for reservoir characterization and modeling.
The concept of reservoir petrofacies
Reservoir petrofacies are defined by the combination of specific depositional structures, textures, and primary composition, with dominant diagenetic processes. The combination of primary textural and compositional aspects with specific diagenetic processes and products corresponds to defined ranges of porosity and permeability values, as well as characteristic logs and seismic signatures. The concept of reservoir petrofacies is a tool for the systematic recognition of these main petrographic attributes that control petrophysical and geophysical behaviors, which ultimately define the evaluation of rocks, rock bodies, and units during oil exploration and production.
Method for the definition of reservoir petrofacies
The recognition of the petrofacies of the reservoir begins with a detailed petrography of representative samples of the area/unit studied. Quantitative modal analysis counting 300 or more points is important, but not always essential for petrofacies recognition, because in some cases the main patterns can be recognized directly through a merely qualitative description. Samples are separated into groups, first based on sedimentary structures, texture, and tissue (grain size, classification, roundness, packing, and orientation). These primary attributes control the original porosity and permeability, which in some cases did not change substantially after deposition. However, most of the deposits show an important modification of the original quality by diagenesis. Therefore, compositional attributes such as types, volume, and location of primary constituents (which directly affect diagenetic processes), types, volume, location, habits, and paragenetic relationships of diagenetic constituents and processes, and consequent types pore size, location, and relationships must also be evaluated. Samples should be grouped taking into account the overlap of depositional structure/texture/tissue attributes with the main primary compositional categories and with the distribution of the most influential diagenetic processes. The attributes with the greatest impact on porosity and permeability are recognized, and preliminary petrofacies are signed. The grouping of samples in the same petrofacies assumes that they present a similar petrophysical behavior. A single depositional facies may correspond to several different reservoir petrofacies. For example, a facies consisting of the same moderately graded, medium to coarse-grained, cross-bedded braided fluvial sandstones may be grouped into different petrofacies, e.g. with a more quartz composition, but strongly cemented by quartz overgrowths, and QzPorous with a composition similar to QzCem but limited cementation and, consequently, porous. Reservoir petrofacies preliminarily defined according to major petrographic attributes are compared to petrophysical and petrographic quantitative parameters using neural network or statistical tools. Threshold values are defined for influential textural and compositional attributes that constrain significant reservoir petrofacies.
Reservoir Petrofacies Application Examples
Uerê Formation, Devonian, Solimões Basin, N Brazil
The Devonian sandstones of the Uerê Formation are important oil exploration targets in the Solimões Basin in the western Brazilian Amazon. The sharp-based progressive sandstones, attributed to a storm-dominated platform complex formed during a general transgressive system tract, are overlain by Frasnian-Famennian black shales. The sandstones are very homogeneous in terms of depositional structures, texture, tissue, and current detrital composition (subarcosas), but extremely heterogeneous in terms of reservoir quality, due to intense diagenesis. Three reservoir petrofacies were recognized, based on packing, porosity, and cementation types (Lima and De Ros, 2002). Petrofacies A is represented by porous sandstones (>15 %; up to 28 %), with preservation of porosity due to inhibition of quartz overgrowth cementation and dissolution under pressure by the grain boundary, eogenetic, microcrystalline quartz or chalcedony. The early diagenetic precipitation of silica was related to the dissolution of sponge spicules, which were concentrated in storm-reworked hybrid sandstones and interbedded spiculite deposits (Lima and De Ros, 2002). Petrofacies B including tight sandstones (5%).
These petrofacies can be effectively plotted on a plot of intergranular volume versus volume of silica cements, showing different ranges of porosity and permeability and logging parameters. Therefore, they can be used to display in three dimensions
the quality of Uerê reservoirs in development reservoirs, as well as, combined with information on their thermal and burial history, to predict the quality of equivalent reservoirs in exploration areas (Lima and De Ros, 2002).
Carapebus Formation, Campos Basin, Eastern Brazil
Four main reservoir petrofacies were recognized in the sandstones and sandy conglomerates of an oil field in the northern Campos Basin. The sandstones were deposited by high-density turbidity currents in complexes of channeled lobes. Petrofacies A comprised poorly sorted, locally conglomeratic, medium to coarse-grained, feldspathic (arc) sandstones and sandy conglomerates that were largely cemented by precompacting crystalline coarse calcite. Consequently, their porosity is commonly totally obliterated, except for some dissolution porosity along the fractures (average 3.2%; up to 10%), and their permeability is very low. Petrofacies B represents the best reservoirs, with good macroporosity (average 27.7%; up to 33.3%) and permeability (up to 1.8 mD), composed of rocks with depositional texture, tissue, and composition equivalent to Petrofacies A, but with little carbonate cementation. , most commonly made up of dolomite in saddle blocks. Secondary porosity due to feldspar dissolution is common. Petrofacies C includes sandstones and coarse sandy conglomerates, commonly conglomeratic, poorly selected, rich in mud intraclasts and carbonaceous fragments, with abundant pseudomatrix generated by the compaction of soft intraclasts. Porosity is low (average 12.1%; up to 13.3%), as is permeability. Petrofacies D is represented by very fine to fine sandstones, well selected, rich in micas and locally small intraclasts of mud. Macroporosity was heterogeneously reduced by compaction (8.3 to 26.3 %; average 18.4 %), but permeability is always low (a few tens to fraction of mD). These reservoir petrofacies are easily recognized in logs and can therefore be used to three-dimensionally represent the quality and heterogeneity of reservoirs in the field.
application concept
The reservoir petrofacies defined by this methodology are consistent in terms of petrophysical porosity and permeability, log, and seismic signatures. Consequently, they can be used to calibrate logs to obtain realistic rock properties. The calibrated logs can then be applied to represent 2D sections and 3D models of true reservoir quality and heterogeneity. Realistic reservoir models built through this methodology can be used in enhanced static and flow simulations during development and production of oil and gas fields. Reservoir petrofacies can be consistently linked to stratigraphic sequence, provenance, and/or burial history parameters for the development of consistent, working models for reservoir quality prediction during hydrocarbon exploration.