Quantification of the bed‐scale architecture of submarine depositional environments

Abstract

Submarine channel and fan deposits form the largest sediment accumulations on Earth and host significant reservoirs for hydrocarbons. While many studies of ancient fan deposits describe architectural variability along 2D transects (e.g. axis‐to‐fringe, proximal‐to‐distal), these relationships are often qualitative and are rarely quantified at the event‐bed scale. In order to enable quantitative comparison of the fine‐scale architecture of submarine depositional environments, 56 bed‐scale outcrop correlation panels from five broadly categorized environments (channel, levee, lobe, channel‐lobe transition zone, CLTZ and basin plain) were digitized. Measured architectural parameters (bed thickness, bed thinning rates, lateral correlation distance, net‐to‐gross) provide a large (n = 28,525) and statistically robust framework to compare event‐bed architectures within and between environments. “Thinning rate” data (i.e. the lateral rate of change of bed thickness) clearly differentiate deposits from different submarine depositional environments, helping to quantify generally accepted models for proximal‐to‐distal evolution of stratigraphic architecture. The thinning rates of sandstone beds and mudstone‐dominated intervals vary predictably between environments. For example, the highest sandstone thinning rates occur in channel deposits (0.2–6 cm/m; P10 and P90 values here and below) and decrease to lobe (0.1–1.6 cm/m), CLTZ (0.2–0.9 cm/m), levee (0.0024–0.078 cm/m) and basin‐plain deposits (0.000017–0.0054 cm/m). These quantitative relationships provide valuable insights for downslope flow evolution and the construction of stratigraphic architecture in submarine settings. Due to intra‐environment variability, net‐to‐gross is highly variable and thus (when considered alone) is not a diagnostic indicator of depositional environment. Submarine lobe deposits show the most variability in event bed thickness, thinning rate and net‐to‐gross, likely due to the inherent facies variability and differing boundary conditions. To explore this variability, lobe deposits were sub‐classified based on position (proximal, distal) and effective confinement (unconfined, semiconfined, confined) to provide a more detailed sub‐environment analysis. Unconfined lobe deposits show a proximal‐to‐distal increase in sandstone thickness and decrease in mudstone thickness, supporting conceptual models. Confined lobe deposits have thicker sandstone and mudstone beds and lower net‐to‐gross values as compared to unconfined and semiconfined lobes, supporting a sediment trapping mechanism by confinement. These quantified bed‐scale parameter comparisons enable the recognition of architectural similarities and differences within and between environments, demonstrating the need for more quantitative studies of bed‐scale heterogeneity. The results from this study are immediately applicable to parameterizing forward stratigraphic models, constraining property distribution in reservoir models, and probabilistic determination of depositional environment ...

Syndepositional tectonics and mass-transport deposits control channelized, bathymetrically complex deep-water systems (Aínsa depocenter, Spain)

“…In FA6, packages of Lf6b can be up to 4 m thick without development of any obvious amalgamation surfaces, although dewatering might obscure them. Where dewatering is not present, maximum bed thickness (between amalgamation surfaces) is rarely > 1.2 m, and is never greater than 2 m, in keeping with typical channel bed thicknesses quoted in Fryer and Jobe (2019). This facies association lies in a continuum between nonamalgamated, incisional sandstones, and heterolithics (FA4) and (FA7).…”

“…However, the constituent sandstones are thinner, laterally more variable, and less amalgamated than those typical of unconfined, sand-rich deposits (cf. Remacha et al, 2005;Liu et al, 2018;Fryer and Jobe, 2019); evidence for compensation is also lacking.…”

Statistical characterization of a confined submarine fan system: The Pennsylvanian Lower Atoka Formation, Ouachita Mountains, USA

“…This method is suitable for the study area because it does not require laterally continuous outcrops as other methods so, e.g. bed tabularity method (Fryer & Jobe, 2019;Liu et al, 2018;Tőkés & Patacci, 2018). The frequency distributions of turbidite sandstone, mudstone, and event beds are expressed by exceedance probability plots, the thick tail of which is a proxy for the degree of confinement (Malinverno, 1997;Marini et al, 2016;Sinclair & Cowie, 2003), and boxplots which compare the thickness quartiles by quadrant.…”

“…The short periodicities (20-30 bpc) are equivalent to 4.6-6.9 m thick packages, using the median event bed thickness of 0.23 m; if the mean thickness (0.44 m) is used, the packages would be 8-13 m. These thickness ranges are typical for partially-confined lobes (elements) worldwide (Prélat et al, 2010;Pettinga et al, 2018;Fryer & Jobe, 2019). Considering the lobe-dominated nature of the lower Atoka, it is interpreted that the short periodicities originate mainly from cyclic lobe avulsions and potentially also high-frequency extrinsic controls.…”

“…Statistical methods based on bed-by-bed measurements have become increasingly insightful in understanding several key aspects of submarine fan systems. These aspects include the discrimination of the fan subenvironments (Carlson & Grotzinger, 2001;Chen & Hiscott, 1999a;Clark & Steel, 2006;Felletti & Bersezio, 2010b;Fryer & Jobe, 2019;MacDonald, 1986;Malinverno, 1997;Sylvester, 2007), quantification or semi-quantification of the degree of confinement (Felletti & Bersezio, 2010a;Fryer & Jobe, 2019;Liu et al, 2018;Marini et al, 2016;Sinclair & Cowie, 2003;Tőkés & Patacci, 2018), objective detection of stratigraphic patterns (Chen & Hiscott, 1999a;Amy et al, 2000;Wilkinson et al, 2003;Sumner et al, 2012;Burgess, 2016a;, and separation of the influence of potential control mechanisms (Weltje & Boer, 1993;Stow et al, 2002;Dennielou et al, 2006;Starek & Fuksi, 2017;Burgess et al, 2019).…”