Research paperSpatial variability in depositional reservoir quality of deep-water channel-fill and lobe deposits
Introduction
Submarine fans represent large volumes of terrigenous sediment transported from the continental shelf to the slope and basin floor (e.g. Emmel and Curray, 1983; Piper et al., 1999; Talling et al., 2007; Prélat et al., 2010; Clare et al., 2014). Modern deep-marine systems are repositories for anthropogenically derived sediment and pollutants, and organic matter (e.g. Galy et al., 2007; Saller et al., 2008; Hodgson, 2009; Gwiazda et al., 2015), and buried systems form reservoirs for groundwater and hydrocarbons, as well as economic accumulations of minerals (e.g. Pettingill, 1998; Ruffell et al., 1998; Weimer et al., 2000; McKie et al., 2015). Consequently, understanding the distribution of depositional facies and their porosity and permeability is key to understanding the distribution and stability of subsurface fluids and minerals (Lien et al., 2006; Porten et al., 2016; Southern et al., 2017).
The porosity of unconsolidated sediments is controlled by the grain-size, sorting and packing of grains (Fraser, 1935; Beard and Weyl, 1973; Hirst et al., 2002; Lien et al., 2006; Njoku and Pirmez, 2011; Porten et al., 2016), whereas detrital clay content, clay mineralogy, and clay distribution have a strong control on permeability (e.g. Wilson, 1992; Hirst et al., 2002; Lien et al., 2006; Ajdukiewicz et al., 2010; Dowey et al., 2012; Porten et al., 2016). These relationships are demonstrated in terrestrial and shallow-marine deposits (e.g. Pryor, 1973; Haile et al., 2017). However, the general inaccessibility of modern deep-water systems means the primary distribution of their textural characteristics is less-well understood.
Controls on reservoir quality operate on a range of scales. At the largest-scale, sandstone reservoir quality is determined by the volume of the deposit and connectivity, as elements include both sand and non-sand reservoir (e.g. Kerr and Jirik, 1990; Hardage et al., 1996; Afifi, 2005; Jolley et al., 2010; Kilhams et al., 2015; Lan et al., 2016). Within the sandstone portion of the reservoir, ‘quality’ is predominantly determined by grain-scale porosity and permeability (e.g. Fraser, 1935; Marzano, 1988; Ramm and Bjørlykke, 1994; Ehrenberg, 1997; Worden et al., 2000; Marchand et al., 2015; Porten et al., 2016), which is modified by eodiagenetic and mesodiagenetic processes (e.g. Ehrenberg, 1989; Pittman and Larese, 1991; Ramm and Bjørlykke, 1994; Ehrenberg, 1997; Worden et al., 2000). It is recognized that the primary texture of depositional facies in deep-water sandstones can also maintain a strong control even after diagenesis (Hirst et al., 2002; Lien et al., 2006; Njoku and Pirmez, 2011; Kilhams et al., 2012; Marchand et al., 2015; Porten et al., 2016). “Depositional reservoir quality” is the initial reservoir potential of a sedimentary accumulation prior to post-depositional modification (Porten et al., 2016). The type of flow that generates a deposit has a strong influence on its texture (Hirst et al., 2002; Lien et al., 2006; Njoku and Pirmez, 2011; Kilhams et al., 2012; Porten et al., 2016; Kane et al., 2017). Therefore, the primary texture of deposits from discrete flow-types can also maintain a strong control during all stages of diagenesis (Hirst et al., 2002; Lien et al., 2006; Njoku and Pirmez, 2011; Kilhams et al., 2012; Marchand et al., 2015; Porten et al., 2016).
Deep-water systems consist of depositional elements, which are hierarchically organized (e.g. Mutti and Ricci-Lucchi, 1972; Mutti, 1985; Mutti and Normark, 1987; Clark and Pickering, 1996; Sprague et al., 2002; Deptuck et al., 2008; Prélat et al., 2009; Di Celma et al., 2011), the organization of which controls the overall size and connectivity of a reservoir. Architectural elements are determined by their size, architecture, bounding surfaces, and relationship to other architectural elements (e.g. Miall, 1985; Mutti and Normark, 1987; Clark and Pickering, 1996; Sprague et al., 2002; Prélat et al., 2009). Individual depositional facies have variable grain-scale textures, and therefore the spatial arrangement of these depositional facies within an architectural element will determine reservoir potential distribution at that hierarchical level. The stacking of architectural elements and their inherited grain-scale texture allows prediction of reservoir quality at higher levels in the architectural hierarchy. Therefore, understanding facies distribution and grain-scale character is critical to improved prediction of reservoir distribution. Previous publications related to the integration of architectural- and grain-scale observations typically consider broad proximal-to-distal trends, or consider facies variability with limited spatial control (Hirst et al., 2002; Lien et al., 2006; Njoku and Pirmez, 2011; Kilhams et al., 2012; Marchand et al., 2015; Porten et al., 2016). Geochemical and mineralogical variations have been recognized within a deep-water channel complex and attributed to the primary texture (Aehnelt et al., 2013). However, no published work has attempted to constrain the depositional reservoir quality within a single architectural element. To assess this issue the following research questions will be addressed: 1) How can an architectural element be characterized at grain-scale? 2) How does reservoir potential vary spatially within an individual architectural element? 3) How do sediment gravity flow processes influence depositional reservoir quality and its distribution?
Section snippets
Geological setting
During the Early Eocene the Aínsa-Jaca Basin developed as an east-west trending, southward migrating foredeep (Puigdefàbregas et al., 1975; Mutti, 1984, 1985; Labaume et al., 1985; Mutti et al., 1988; Muñoz, 1992; Teixell and García-Sansegundo, 1995). The deep-water deposits form the Hecho Group (Fig. 2; Mutti, 1985). The Aínsa Basin fill predominantly consists of submarine slope channel systems and mass-transport deposits, separated by marlstones (e.g. Mutti, 1977; Clark et al., 1992; Mutti,
Methods
Different stratigraphic correlations between the Aínsa and Jaca Basins have been proposed (Mutti, 1984, 1985, 1992; Remacha et al., 2003; Das Gupta and Pickering, 2008; Caja et al., 2010; Clark et al., 2017). Following Das Gupta and Pickering (2008), the Gerbe (Aínsa) and Broto (Jaca) Systems are considered as broadly equivalent and are studied here. Whilst uncertainty remains with this correlation, the two systems form part of the genetically related wider basin-fill, have the same burial
Facies
Lithofacies are summarized in Table 1 and grouped into facies associations in Table 2:
Architectural element interpretations
The geometrical relationships established in the stratigraphic correlations of Fig. 4, Fig. 5, and the facies associations described in Table 2, are used to interpret the environment of deposition of the Gerbe and Broto architectural elements.
Results
Architectural and textural data were collected for both the channel-fill element and lobe. Textural properties are split into facies associations for each architectural element to enable comparison of architectural and textural properties, and consequent depositional reservoir quality in different sub-environments within deep-water systems.
Spatial variation in depositional reservoir quality
The distribution of textural properties (grain-size, sorting and matrix content) within architectural elements is a first-order control on the initial depositional porosity and permeability of sandstones (e.g. Fraser, 1935; Beard and Weyl, 1973; Hirst et al., 2002; Lien et al., 2006; Njoku and Pirmez, 2011; Kilhams et al., 2012; Marchand et al., 2015; Porten et al., 2016; Southern et al., 2017) and provides insight into the depositional reservoir quality within the study area. The effects of
Conclusions
Two deep-water architectural elements, a channel-fill element and a lobe, are characterized at grain-scale using quantitative methodology to map depositional reservoir quality spatially within individual architectural elements for the first time. Quantification of these data and their rates of change can be important parameters for sub-surface predictability and fluid flow simulation models. Textural and architectural properties show strong spatial variation in both elements. The distribution
Acknowledgments
The authors are grateful to the AAPG Foundation Grants-In-Aid program for the award of the R. Dana Russell memorial grant, which part-funded this research. We thank Salvatore Critelli for his editorial handling of the paper and the reviews of Salvatore Milli and an anonymous reviewer whose comments enhanced the clarity of the manuscript.
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