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Numerical forward modelling of ‘fluxoturbidite’ flume experiments using Sedsim
Marine and Petroleum Geology 35 (2012) 190e200
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Marine and Petroleum Geology journal homepage: www.elsevier.com/locate/marpetgeo
Numerical forward modelling of ‘fluxoturbidite’ flume experiments using Sedsim Xiu Huang a, b, *, Chris Dyt b, Cedric Griffiths b, Tristan Salles b a b
China University of Geosciences (Beijing), Beijing, China CSIRO, Predictive Geoscience Group, Kensington, Western Australia, Australia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 July 2011 Received in revised form 2 January 2012 Accepted 7 February 2012 Available online 27 February 2012
Delta front ‘fluxoturbidity deposits’ in rift basin margins, as well as sand avalanches, are influenced by topography, water level fluctuation and wave action. Instability of prior sediments deposited on the delta front is a prerequisite for the generation of ‘fluxoturbidity’. In this study Sedsim, a three-dimensional numerical stratigraphic forward model is used to replicate and extend a set of physical flume tank experiments investigating the formation of ‘fluxoturbidites’ from different initial conditions. This study has investigated the influence of topographic slope, relative water level change and wave action on the formation of ‘fluxoturbidites’, and hopefully improves our understanding and insight into the dynamic processes of ‘fluxoturbidity’ resulting from different initiation mechanisms. The study also illustrates the value of numerical modelling in complementing and extending physical flume tank studies. We show that, at least at the scale of a flume tank, there exists an optimum window of topographic slopes within which ‘fluxoturbidites’ arise due to slumping. This window, ranging from 9 to 18 , may be useful in distinguishing the effects of topographic slope from other ‘fluxoturbidite’ causal mechanisms. The amplitude and frequency of water level oscillation appears to be a significant control on ‘fluxoturbidite’ thickness, while wave attributes, especially wave angles, appear to affect ‘fluxoturbidite’ locality relative to the sediment source. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: ‘Fluxoturbidite’ Sand avalanche Sedsim Stratigraphic forward model Topographic slope Relative water level change Wave action Flume tank
1. Introduction ‘Fluxoturbidite’ is a term that has controversial origins and doubtful utility without further definition. Shanmugam (2006) states “The term fluxoturbidite was introduced by Dzulynskl et al. (1959). The origin of these deposits is unclear. These deposits appear to represent sand avalanches, slumps, and other mass movements.” After investigating the meaning of the term fluxoturbidite, Hsü (1989, p. 85), points out that “.this is another case when a geologist wanted to hide his ignorance behind an exotic name.” The Allaby’s in their 1999 Dictionary of Earth Science entry define fluxoturbidite as: “A poorly graded sediment, the product of gravity-induced flow in which little turbulent mixing of particles occurs. It is transitional between a slump and a turbidity flow.” While neither defending the use of the term, nor wishing to add to the confusion about whether it describes a facies or a process, the aim of this paper is simply to demonstrate the use of a numerical stratigraphic forward model to replicate and extend a particular
* Corresponding author. China University of Geosciences, Beijing, China. Fax: þ618 6436 8555. E-mail address: [email protected] (X. Huang). 0264-8172/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.marpetgeo.2012.02.012
flume experiment that investigated the generation of sand avalanches down a delta slope, and the deposition of those avalanches at the base of slope. The term ‘fluxoturbidite’, widely used in China, is considered synonymous with ‘sand avalanche’ in this paper. Stanley and Bouma (1964), in their Fig. 17, show deposits labelled as ‘fluxoturbidite’ at the mouth of a submarine channel. One can imagine that such sand avalanches can be initiated by many types of event: autochthonous instability, tectonic uplift, earthquakes, storms, etc. It is commonly agreed that instability of sediments deposited on the upper delta front is the main prerequisite for the generation of sand avalanches or ‘fluxoturbidites’ (Chang, 1991; Li et al., 2003b; Zhang, 2004; Niu et al., 2008; Jiang et al., 2008; Chen et al., 2009). Therefore, it is not surprising that many studies of such instability have focused on earthquakes triggering seismogenic slumping (e.g., Heezen and Ewing, 1952; Coleman and Prior, 1988; Hampton et al., 1996; Li, 2005; Zhang et al., 2006; Dadson et al., 2005). Other writers have suggested that storm, wave and water level oscillation also play significant roles in inducing sediment detachment (e.g. Andresen and Bjerrum, 1967; Morgenstern, 1967; Bjerrum, 1971; Piper et al., 1985a,b; Prior et al., 1989; Weaver et al., 1992; Garcia and Hull, 1994; Lee and Chough, 2001; Xu et al., 2004; Li, 2005; Zhang et al., 2006; Warrick et al., 2008; Salles et al., 2008;
X. Huang et al. / Marine and Petroleum Geology 35 (2012) 190e200
Figure 1. Cross-section of flume experiment equipment. [Figure taken from Li, 2005].
Uroza and Steel, 2008), but none of the above studies looked at the effect of variations in the direction of these forcing processes. Furthermore, the autochthonous process of oversteepening of the parent deposit on the delta front can be another potential cause of instability within a sedimentary deposit (Banerjee, 1966; Prior et al., 1986, 1987; Li, 2005; Zhang et al., 2006; Girardclos et al., 2007). However, most published models of turbidites on the delta toe and prodelta have focused on the delta-slope morphology, with slopes varying from 6 to 35 (FoÈrstner et al., 1968; Sturm and Matter, 1972; Sturm, 1976; Sturm and Matter, 1978; Bogen, 1983; Kostaschuk and McMann, 1983; Carter, 1988; Corner et al., 1990; Meixner, 1991; Siegenthaler and Sturm, 1991; Jansa, 1991; Mosher et al., 1994). There are also limited data concerning the localised preexisting topographic slope angle in the area where fan-delta systems have developed (Shepard, 1963; Shepard and Dill, 1966), and the effect of preexisting topographic slope angle on natural delta systems is poorly constrained (Lajoie, 1979; Macdonald, 1986). Numerical modelling of ‘fluxoturbidite’ initiation may provide insight into variations of internal architecture of these deposits, and help understand the evolution of ‘fluxoturbidite’ deposition and the location of these potential hydrocarbon reservoir rocks. The aim of this study is to show the application of stratigraphic forward modelling and in particular the ability of the Sedsim computer program (Tetzlaff and Harbaugh, 1989; Griffiths and Paraschivoiu, 1998) to capture some of the significant features of sand avalanches at flume tank scale, and investigate potential causal processes such as topographic slope, water level fluctuation and wave action. Reference examples of the modelling potential and capabilities are illustrated by numerically replicating physical flume experiments. Numerical results from Sedsim models are compared with the experimental results from the physical flume tank in order to validate the model capabilities. Eventually, three of the many possible causal mechanisms of ‘fluxoturbidity’ are investigated using numerical stratigraphic forward modelling. 2. The Sedsim program Sedsim is a three-dimensional numerical stratigraphic forward model that simulates a wide variety of depositional processes (e.g., sediment erosion, transport, and deposition) both on geological and engineering time scales. It is a process model which uses a marker-in-cell approach presented by Tetzlaff and Harbaugh (1989) to overcome the limitations caused by the difficulty of solving the full NaviereStokes equations and the continuity
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equation. This approach combines the attributes of Eulerian and Lagrangian equations and allows Sedsim to successfully simulate complex depositional systems and predict the erosion, transport and deposition of sediment in three spatial dimensions over time. A series of Sedsim modules simulating sediment transport, subsidence, flexural isostatic loading, compaction, slope failure and other processes, are represented by Tetzlaff and Harbaugh (1989), Griffiths and Paraschivoiu (1998), Griffiths et al. (2001), Liang et al. (2005), Li et al. (2004, 2006, 2007, 2008) and Salles et al. (2010). Sedsim is controlled by a number of input parameters, for example, relative sea level/base-level curve, initial topography/ bathymetry, tectonic movement, sediment input rates etc. Sedsim enables a wide variety of dynamic processes to be simulated quickly and thus evaluation of the impact of a range of different parameters can be analysed in a short period of time. Since 1994 Sedsim has been widely applied in hydrocarbon exploration and prediction (e.g., Griffiths and Paraschivoiu, 1998; Griffiths et al., 2001), and numerous researchers have adopted Sedsim to simulate and evaluate a variety of depositional systems. 3. Physical tank experiments The flume experiments (Li, 2005; Zhang et al., 2006) were conducted using a 5 m long by 2 m wide and 1 m deep flume (Fig. 1). The initial ramp was 0.6 m high 3.5 m long and 2 m wide, with a slope of 0.16 (9 ). The input parameters of the physical experiments are shown in Table 1. Observed depositional slopes in rift basins in China range from 3 to 20 (Lai and Zhou, 1994; Liu et al., 1995; Feng and Li, 2001; Jin et al., 2003; Du et al., 2005). The sediment released into the tank had a grain-size range within that observed in modern (e.g., Girardclos et al., 2007) and ancient delta systems (e.g., Rao et al., 2004; Yin et al., 2006; Yan et al., 2004), and was carried as a suspended load that consisted of 64.7% clay and 35.3% silt and fine sand. The sediment input was not central in the tank, but was located 60 cm from the glass wall and 50 cm from the steep edge of the tank. The total volume of sediment supply was adjusted so that the delta system was appropriate size for the specific flume tank, as was the water discharge rate. Before the experiments, the ramp and flat basin were covered by a smoothed initial 3 cm layer of sandy sediments. Two experimental runs were carried out. Run 1 investigated ‘fluxoturbidite’ production without external trigger and ran for 1446 min using a water level that rose uniformly from 0.35 m to 0.4 m during the first 230 min to build an initial delta deposit and was unchanged during the last 1216 min (Fig. 2). Run 2 investigated the effect of waves using the result from Run 1 as the initial topography. The waves had an 8 cm height and 4 min period representing moderate wave conditions. They were created by the up and down movements of a container (located at the centre point at the end of tank opposite the sediment supply) in the water (Fig. 1). The parameters of the two runs in this study are listed in Table 1. 4. Sedsim numerical simulations and results There were two goals for the Sedsim numerical simulations. The first was to verify that a numerical simulation could replicate the
Table 1 Input parameters of the flume experiments. (Table taken from Zhang et al., 2006). Experiments
Run 1 Run 2
Trigger
None Wave
Time (min)
1446 1446
Water depth (cm)
35e40 40
Water discharge rate (ml/s)
41.7 41.7
Total sediment (kg)
730 730
Sediment composition (%)
Slope
Coarse
Medium
Fine
Finest
Clay
0 0
5.4 5.4
29.9 29.9
30.4 30.4
34.3 34.3
9 9
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Figure 2. Diagram showing the topography with pre-deposit and water level curve used in Sedsim modelling. The initial ramp is 0.6 m high 3.5 m long and 2 m wide, and the predeposit layer is 3 cm thick (yellow for sandy sediments layer, brown for initial ramp) (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).
Table 2 Parameters and parameter values used in the Sedsim Model. Experiments
Vx (m/s)
Vy (m/s)
Run 1 Experiments Run 2
0 0.0417 Tank time (min) 4
Water discharge rate (m3/s)
Concentration (kg/m3)
1 mm
0.0000417 Waves height (m) 0.08
201.775
0 5.4 Wave base (m) 0.04
physical tank experiments in all essential details (i.e. scale, input parameters and output geometries and grain-size distributions. The second, once we were sure that the physical experiments could be numerically replicated, was to extend the range of the experiments to investigate parameters that were not physically investigated due to lack of time or equipment. The main aim of both the physical and numerical flume tank experiments was to investigate how much the behaviour of the sediment slumping and redeposition leading to ‘fluxoturbidites’ is sensitive to changes in various forcing conditions. Initially we carried out a series of simulations using the input parameters shown in Table 2, based on the parameters reported by Li (2005) and Zhang et al. (2006) at physical flume tank scale (Table 1). The topographic surface with pre-deposit, over which sediment was transported, deposited and eroded, was reproduced at the same scale (Fig. 2). Sediment was introduced to the model in the same location of the tank with volumes of sediment as listed in Table 1. In Run 1, the simulation time ran from 0 to 1446 min with a display interval of 20 min. The spatial resolution was 0.1 m giving a grid of 50 by 20 cells. Sediments supplied to the tank had grain diameters of 1 mm, 0.3 mm, 0.03 mm, and 0.002 mm, with a density of 2650 kg m3. The angle of repose for each grain size was set according to the generalized relationship between sediment grainsize and angle of internal friction (Kirkby, 1987; Nemec, 1990b; Orton and Reading, 1993). Studies of modern and ancient slope
Table 3 Parameters and parameter values used in Sedsim Model. Process
Run
Physical reference (Run 1)
Range
Slope angle Water level change Process Wave action
3e15 16e20 Run 21e30 31e39 40e45
9 0.4 m Reference (Run 2) Wave height: 0.08 m Wave base: 0.04 m Wave direction: 0
7 e20 0.35e0.4 m Range 0.04e0.4 m 0.02e0.1 m 10 e60
0.3 mm
0.03 mm
0.002 mm
29.9 64.7 Wave direction 0
failures indicated that the critical value for fine sandy deposits is up to 30 (Orton and Reading, 1993), to between 25 and 10 within sand- and silt- dominated deposits and clay- dominated deposits, respectively (Kirkby, 1987). Sediment failure can occur due to the autochthonous process of oversteepening. For Run 2, the Sedsim wave module was included, which generated waves uniformly southwards from the northern edge with the same amplitude and period as the physical model (Li, 2005; Zhang et al., 2006). The initial topography for Run 2 was the final surface from Run 1. In the following, three different series of numerical simulations have been run with a variety of parameters giving different initial conditions for the detachment (e.g., topographic slope, water level fluctuation and wave actions, see Table 3).
4.1. The reference models The numerical model predictions for ‘fluxoturbidite’ deposition are plotted together with the physical experimental results in Figures 3e6. In Run 1, during the time from the beginning of the run, the simulation results as shown in Figure 3 indicate that the delta system initially aggrades (A), during which sands are deposited mainly on the delta plain, whereas silt and clay are transported to the delta front; slumping begins at (B), and continues (CeD), producing numerous slump scars around the delta front, gullies in the prodelta (E, F), and ‘fluxoturbidite’ terraces below the delta front (Fig. 6), despite the constant water level. The resultant morphology from the simulation (Fig. 3DeF) agrees with the observed physical experimental result (Fig. 4) both in position and form. The length and width of delta plain obtained are of the same order as those observed in the physical experimental data (a length of 1.5 m and width of 2 m are derived from the numerical model as compared to 1.4 m in length and 2 m in width as observed in the physical experiment). The length of delta front and prodelta also compare well with the experimental data (30 cm for 35 cm, 160 cm
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Figure 3. Simulation of the ‘fluxoturbidite’ systems in the delta front without trigger mechanism from the beginning of the run using Sedsim. (A), (B), (C) and (D) show composition of deposits, with colours denoting different grain sizes (yellow for fine sand, green for silt and grey for clay). (E) Shows an oblique view with colours representing locations (green for delta plain, yellow for foreslope and purple for prodelta). (F) Shows surface representing the final morphology. Vertical exaggeration of (E) and (F) is 4 times horizontal. See text for discussion (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).
for 175 cm obtained, respectively). Figure 3D also shows a greater distance of 3.9 m clay sediments with ligulate shape, which are perpendicular to the shoreline and extent to the slope break. In comparison, the physical experiment yields a maximum transport distance of 4 m. The results shown in Figure 5 indicate that the features of multiple foreset deposits with compaction and depression due to the diversion of channels seen in the physical experiment (Fig. 5B) are captured well by the numerical model (Fig. 5A). Furthermore, the maximum thickness and slope angle of the silt and sand dominated delta front (Fig. 5A), match the results shown in Figure 5B (maximum thickness of 12.75 cm for 13 cm observed and slope angle of 27.5 for 27 observed). The ‘fluxoturbidite’ deposits in the prodelta (Fig. 6) are in accordance with the deposits observed in the physical experiment (Fig. 4). However, some load structures in the physical experiment, such as pillow structures and flame structures in liquefied zone (Fig. 5B), cannot be produced directly in the numerical forward model (Fig. 5A) as the height of these structures is small with
respect to the flow depth and they have an area less than the simulation grid dimension. Nevertheless, over the same time scale, and allowing for aliasing effects of the grid size used, the simulation represents a reasonable recreation of sedimentary deposition in term of depocenter location, geometry and thickness. One feature to note concerning the difference between the physical and numerical experiments is the elapsed time for the experiments. Run 1 took less than 6 minutes of elapsed computer time to replicate the 1446 min of physical experimental time. In Run 2 the result of numerical simulation of wave action is consistent with the observed experimental results. The delta system is characterized by four obvious terraces (Figs. 7 and 8) including a delta plain unaffected by wave shadow, the liquefied zone in the delta front, the prodelta where most sandy sediment resettled, and the deep-water basin reached by some muddy sediment. Their lengths are close to those observed in the physical experiments (1.3 m for 1.2 m, 0.2 m for 0.3 m, 1.5 m for 1.5 m, and 1.5 m for 1.4 m observed, respectively). Figure 9 shows that the
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Figure 6. Perspective fence display of sections showing ‘fluxoturbidity’ deposits, which are denoted by arrows, in front of the delta front showing Sedsim predicted results. Vertical exaggeration is 4 times horizontal.
Figure 4. Sketch of the ‘fluxoturbidite’ systems in front of the delta front without trigger mechanism in flume tank. See text for discussion. From Li (2005).
theoretical value of the maximum wave base agrees with the experimental value. However, some features of the prodelta in the numerical model do not match the physical experimental results. The most
noteworthy difference is that the maximum transport distance between ‘fluxoturbidies’ and finger-shape sands is a little longer in the numerical model than in experiment. This discrepancy between the simulation and observation is probably due to the distribution of wave energy. The wave direction in the numerical simulation is a linear wave field from south to north whereas in the physical experiment the waves were created by a point source with energy propagated radially (Li, 2005; Zhang et al., 2006). Despite this minor discrepancy, the numerical simulation has captured the
Figure 5. (A) Cross section, which is perpendicular to shore compares with (B) flume experiment result which from Li (2005). , , , ¼ multiple foreset deposits. Colours represent range of grain sizes in Figure 3. See text for discussion (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).
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Figure 7. Simulation of the distribution of foreset deposition with waves using Sedsim modelling. (A), (B), (C) and (D) show composition of deposits at wateresediment interface. Colours represent range of grain sizes in Figure 3. (E) Shows surface representing the final morphology. The maximum transport distance between ‘fluxoturbidites’ and finger-shape sands is approximately 60 cm. Vertical exaggeration of (E) is 2 times horizontal. See text for discussion (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).
sediment movement, geometry and ‘fluxoturbidite’ distribution. Run 2 took 1 second of elapsed computer time to replicate the 4 min of physical experimental time.
4.2. The influence of topographic slope on ‘fluxoturbidite’
Figure 8. The distribution of foresets deposition with wave. The maximum transport distance between ‘fluxoturbidies’ and finger-shape sands is about 55 cm. From Li (2005).
Using the same parameters as used in the Run 1 reference model described above, but varying topographic slopes from 7 to 20 , allowed the investigation of the possible influence of topographic slope on ‘fluxoturbidite’ deposition as a response to slumping. Figure 10 clearly shows the relationship between slope angle and slump occurrence, and indicates that there is an optimum window of topographic slopes within which slumping leading to ‘fluxoturbidites’ occurs. Below 9 and above 18 slumping does not occur or generate ‘fluxoturbidites’ under the experimental conditions. The silt- and sand dominated slope profiles with topography of 7 and 8 in this study have maximum delta slope angles of 22.5 and 24.5 . These slope angles agree with the result suggested by Kirkby (1987), who suggested that the critical slope for slumping to be initiated within loosely packed silt and fine sand is generally greater than 25 . That slumping does not occur on such steep slopes is possibly due to initial sediment bypass enabling silt and sand dominated sediments to settle directly at the toe of slope without slope failure. This result might explain the difference between ‘fluxoturbidies’ induced by steep topographic slopes, especially extremely precipitous slopes, and any other mechanism (e.g., Bogen, 1983; Kostaschuk and McMann, 1983; Corner et al., 1990). As a consequence, the critical values of topographic slope for ‘fluxoturbidite’ deposition via slumping lie between 9 and 18 . It remains to be tested whether or not this gradient window is also present at full scale.
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Figure 9. (A), perspective fence of liquefied zone (Pseudo-seismic) with Sedsim modelling, compares with (B) flume experiment result which was from Li (2005). The maximum wave base is 4 cm both in simulation and experiment result.
Figure 10. Relation between topographic slope activity and slumping.
4.3. The influence of relative water level change on ‘fluxoturbidites’ What happens to simulated ‘fluxoturbidite’ sand thicknesses with water level changes? There are no water level oscillations in the reference model, the thicknesses slumped over time as shown
in Figure 11 show that slump occurrence is infrequent without a forcing event. Figures 12e14 show the thicknesses slumped over time for two model runs with a wave amplitude of 0.05 m and a period of eustatic water level oscillation ranging from 1.0 T (T ¼ 834 min) to
Figure 11. Thickness slumped from the standard reference model, plotted against elapsed model time and alongside the water level curve.
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Figure 12. The thickness slumped through time with an amplitude 0.05 m and eustatic period 1.0T (T ¼ 834 min).
Figure 13. The thickness slumped through time with an amplitude 0.05 m and eustatic period 0.5T (T ¼ 417 min).
0.5 T. The results reveal that in both runs, an increase of slumping resulting in ‘fluxoturbidity’ occurs during relative water level fall, whereas little slumping occurs when relative water level rose. Figure 13 also indicates that it is easier to initiate slumping prior to the second relative water level fall than the first. By the time of the first rise, steeper slopes had been established because of delta progradation so that the slumping was more readily initiated. A rapid increase in the frequency of slumping events is observed as the rate of relative water level fall increases (Fig. 14). Possibly because of the rapid rate of fall, sediments have less time to stabilise, and thus slumping occurs frequently. Figure 15 demonstrates that the amplitude and frequency of relative water level oscillations can have significant effects on the change of total sand thickness slumped. For the same amplitude the total sand thickness slumped is always higher with high frequency than with low frequency. Although the results of no- and lowamplitude models suggest that the slumping can happen without relative water level change, the incremental total sand thickness slumped is proportional to amplitude of water level change.
Consequently, the numerical analysis indicates that, at least under these tank conditions, the amplitude and frequency of water level oscillation are the most important parameters influencing ‘fluxoturbidite’ thickness. High amplitude and high frequency of water level oscillation usually leads to frequent slumping which is one cause of ‘fluxoturbidites’. 4.4. The influence of wave action on ’fluxoturbidite’ production The Sedsim model simulations are performed with a range of wave parameters, including wave height, wave base and wave direction as presented in Table 1. Figures 16e18 illustrate that variation in wave parameters plays an important role in the geographic distribution of ‘fluxoturbidite’ deposition. It is observed that the area of the sediment reworked zone significantly enlarges and migrates shoreward with increasing wave height (Fig. 16), reworking the previous delta plain deposits. Unlike the wave height influence, the effect of changing wave-base tends to change the transport distance of reworked sediments
Figure 14. The thickness slumped through time with the rate of change of water level.
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Figure 15. The total thickness slumped plotted against the amplitude of water level oscillation. Results are taken from the 21 separate model runs.
Figure 16. Sandy deposit thickness reworked due to the change of wave height ((A) for 0.04 m, (B) for 0.08 m, and (C) for 0.4 m, respectively).
Figure 17. Sandy deposit thickness reworked due to the change of wave base. The wave base is 0.02 m in (A), 0.04 m in (B) and 0.2 m in (C).
Figure 18. The distribution of sandy deposit thickness reworked with different wave angles (0 in (A), 30 in (B) for, and 60 in (C), respectively).
X. Huang et al. / Marine and Petroleum Geology 35 (2012) 190e200
(Fig. 17), with deeper wave-base leading to greater transport distance. Figure 18 shows that the distribution of ‘fluxoturbidite’ deposits induced by wave action is significantly affected by wave direction. Sandy sediments redeposit asymmetrically around the prodelta with wave angles ranging from 0 to 60 , and become digitate farther from delta plain, possibly having a significant effect on lateral reservoir continuity. 5. Conclusions Using the Sedsim stratigraphic forward modelling program, we simulated and validated recent interpretations concerning ‘fluxoturbidite’ generation and distribution in a series of physical flume tank experiments. The following can be concluded from the investigation: (1) Initial topographic slope angle significantly influences the evolution of ‘fluxoturbidite’ deposits. There may exist an optimum window of initial topographic slopes for the production of slump-initiated ‘fluxoturbidites’. This window was between 9 and 18 for the particular grain size distribution used in these experiments. (2) Rates of relative water level change play an important role in governing the occurrence of slump-initiated ‘fluxoturbidity’. The generation of ‘fluxoturbidites’ is more sensitive to water level falls. The amplitude and frequency of water level oscillations appear to influence ‘fluxoturbidite’ thickness. (3) Wave action, and in particular the incident angle, may influence the location and reservoir continuity of sand avalanche/ ‘fluxoturbidite’ reservoirs. (4) The numerical simulations took around 1/261 the time of the physical experiments, provided easy access to the internal geometries and facies distributions, and enabled the effects of a greater range of parameters to be studied in a shorter time interval. Numerical simulation provided a convenient tool for investigating a wide range of dynamic processes resulting in ‘fluxoturbidite’ deposition, and shows how a numerical model may complement physical flume tank studies. Acknowledgements We would like to thank C. Li of SINOPEC Sheng Li Oilfield for giving the experiment dataset and we also deeply thank K. Liu and X. Qi of CSIRO Earth Science and Resource Engineering, and F. Li Western Australia Dept of Transport for their very useful reviews and comments for the improvement of the paper. References Andresen, A., Bjerrum, L., 1967. In: Rmhards, A.F. (Ed.), Slides in Subaqueous Slopes in Loose Sand and Silt. Marine Geotechnique Uinv I11Press, Urbana, Chicago, pp. 221e239. Banerjee, I., 1966. Turbidites in a glacial sequence: a study from the Talchir Formation, Raniganj Coalfield, India. J. Geol. 5 (74), 593e606. Bjerrum, L., 1971. Subaqueous slope failures in Norwegian fjords. Geotechnology, 1e8. Bogen, J., 1983. Morphology and sedimentology of deltas in fjord and fjord valley lakes. Sediment. Geol. 36, 245e267. Carter, R.M., 1988. Post-breakup stratigraphy of the Kaikoura Synthem, CretaceousCenozoic), continental margin, southeastern New Zealand. NZ J. Geol. Geophys. 31, 405e429. Chang, C.Y., 1991. Geological characteristics and distribution patterns of hydrocarbon deposits in the Bohai Bay Basin, east China. Mar. Petrol. Geol. 8, 98e106. Chen, D.X., Pang, X.Q., Jiang, Z.Z., Zeng, J.H., Qiu, N.S., Li, M.W., 2009. Reservoir characteristics and their effects on hydrocarbon accumulation in lacustrine turbidites in the Jiyang Super-depression, Bohai Bay Basin, China. Mar. Petrol. Geol. 26, 149e162.
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Shepard, F.P., 1963. Submarine Geology. Harper and Row, New York, pp. 557. Siegenthaler, C., Sturm, M., 1991. Slump induced surges and sediment transport in Lake Uri, Switzerland. Verh. Int. Ver. Theor. Angew. Limnol. 24, 955e958. Stanley, D.J., Bouma, A.H., 1964. Methodology and paleogeographic interpretation of Flysch formations: a summary of studies in the maritime Alps. In: Bouma, A.H., Brouwer, A. (Eds.), Turbidites. Elsevier, Amsterdam, pp. 34e64. Sturm, M., Matter, A., 1972. Sedimente und Sedimentationsvorga Ènge im Thunersee. Ecol. Geol. Helv. 65, 563e590. Sturm, M., Matter, A., 1978. Turbidites and varves in Lake Brienz, Switzerland): deposition of clastic detritus by density currents. In: Matter, A., Tucker, M.E. (Eds.), Modern and Ancient Lake Sediments. Int. Assoc. Sedimentol. Spec. Publ, vol. 2, pp. 147e168. Sturm, M., 1976. Die OberflaÈchen sedimente des Brienzersees. Ecol. Geol. Helv. 69, 111e123. Tetzlaff, D.M., Harbaugh, J.W., 1989. Simulating Clastic Sedimentation; Computer Methods in Geosciences. Van Nostrand Reinhold, New York, pp. 196. Uroza, C.A., Steel, R.J., 2008. A highstand shelf-margin delta system from the Eocene of West Spitsbergen, Norway. Sediment. Geol. 203, 229e245. Warrick, J.A., Xu, J., Noble, M.A., Lee, H.J., 2008. Rapid formation of hyperpycnal sediment gravity currents offshore of a semi-arid California river. Cont. Shelf Res. 28, 991e1009. Weaver, P.P.E., Rothwell, R.G., Ebbing, J., Gunn, D., Hunter, P.M., 1992. Correlation, frequency of emplacement and source directions of megaturbidites on the Madeira abyssal plain. Mar. Geol. 109, 1e20. Xu, J.P., Noble, M., Rosenfeld, L.K., 2004. In-situ measurements of velocity structure within turbidity currents. Geophys. Res. Lett. 31, L09311. Yan, J.H., Chen, S.Y., Song, G.Q., Jiang, Z.X., Qiu, G.Q., 2004. Preliminary study on the formation of Fluxoturbidite in Front of Delta. Acta Sedimentol. Sin. 22 (4), 573e578 (in Chinese with English abstract). Yin, T.J., Zhang, C.M., Li, Z.C., 2006. Depositional characteristics of Fluxoturbidite and exploration technologies of subtle reservoirs in the Dongying sag. Oil Gas Geol. 27 (1), 93e98 (in Chinese with English abstract). Zhang, G.L., Chen, S.Y., Yan, J.H., Jiang, A.X., Song, G.Q., Qiu, G.Q., 2006. Simulation of Fluxoturbidite in Front of Delta. Acta Sedimentol. Sin. 24 (1), 50e55 (in Chinese with English abstract). Zhang, S.W., 2004. The application of integrated approach in exploration of lacustrine turbidites in Jiyang sub-basin, Bohai Bay Basin, China. J. Petrol. Sci. Eng. 41, 67e77.
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Marine and Petroleum Geology journal homepage: www.elsevier.com/locate/marpetgeo
Numerical forward modelling of ‘fluxoturbidite’ flume experiments using Sedsim Xiu Huang a, b, *, Chris Dyt b, Cedric Griffiths b, Tristan Salles b a b
China University of Geosciences (Beijing), Beijing, China CSIRO, Predictive Geoscience Group, Kensington, Western Australia, Australia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 July 2011 Received in revised form 2 January 2012 Accepted 7 February 2012 Available online 27 February 2012
Delta front ‘fluxoturbidity deposits’ in rift basin margins, as well as sand avalanches, are influenced by topography, water level fluctuation and wave action. Instability of prior sediments deposited on the delta front is a prerequisite for the generation of ‘fluxoturbidity’. In this study Sedsim, a three-dimensional numerical stratigraphic forward model is used to replicate and extend a set of physical flume tank experiments investigating the formation of ‘fluxoturbidites’ from different initial conditions. This study has investigated the influence of topographic slope, relative water level change and wave action on the formation of ‘fluxoturbidites’, and hopefully improves our understanding and insight into the dynamic processes of ‘fluxoturbidity’ resulting from different initiation mechanisms. The study also illustrates the value of numerical modelling in complementing and extending physical flume tank studies. We show that, at least at the scale of a flume tank, there exists an optimum window of topographic slopes within which ‘fluxoturbidites’ arise due to slumping. This window, ranging from 9 to 18 , may be useful in distinguishing the effects of topographic slope from other ‘fluxoturbidite’ causal mechanisms. The amplitude and frequency of water level oscillation appears to be a significant control on ‘fluxoturbidite’ thickness, while wave attributes, especially wave angles, appear to affect ‘fluxoturbidite’ locality relative to the sediment source. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: ‘Fluxoturbidite’ Sand avalanche Sedsim Stratigraphic forward model Topographic slope Relative water level change Wave action Flume tank
1. Introduction ‘Fluxoturbidite’ is a term that has controversial origins and doubtful utility without further definition. Shanmugam (2006) states “The term fluxoturbidite was introduced by Dzulynskl et al. (1959). The origin of these deposits is unclear. These deposits appear to represent sand avalanches, slumps, and other mass movements.” After investigating the meaning of the term fluxoturbidite, Hsü (1989, p. 85), points out that “.this is another case when a geologist wanted to hide his ignorance behind an exotic name.” The Allaby’s in their 1999 Dictionary of Earth Science entry define fluxoturbidite as: “A poorly graded sediment, the product of gravity-induced flow in which little turbulent mixing of particles occurs. It is transitional between a slump and a turbidity flow.” While neither defending the use of the term, nor wishing to add to the confusion about whether it describes a facies or a process, the aim of this paper is simply to demonstrate the use of a numerical stratigraphic forward model to replicate and extend a particular
* Corresponding author. China University of Geosciences, Beijing, China. Fax: þ618 6436 8555. E-mail address: [email protected] (X. Huang). 0264-8172/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.marpetgeo.2012.02.012
flume experiment that investigated the generation of sand avalanches down a delta slope, and the deposition of those avalanches at the base of slope. The term ‘fluxoturbidite’, widely used in China, is considered synonymous with ‘sand avalanche’ in this paper. Stanley and Bouma (1964), in their Fig. 17, show deposits labelled as ‘fluxoturbidite’ at the mouth of a submarine channel. One can imagine that such sand avalanches can be initiated by many types of event: autochthonous instability, tectonic uplift, earthquakes, storms, etc. It is commonly agreed that instability of sediments deposited on the upper delta front is the main prerequisite for the generation of sand avalanches or ‘fluxoturbidites’ (Chang, 1991; Li et al., 2003b; Zhang, 2004; Niu et al., 2008; Jiang et al., 2008; Chen et al., 2009). Therefore, it is not surprising that many studies of such instability have focused on earthquakes triggering seismogenic slumping (e.g., Heezen and Ewing, 1952; Coleman and Prior, 1988; Hampton et al., 1996; Li, 2005; Zhang et al., 2006; Dadson et al., 2005). Other writers have suggested that storm, wave and water level oscillation also play significant roles in inducing sediment detachment (e.g. Andresen and Bjerrum, 1967; Morgenstern, 1967; Bjerrum, 1971; Piper et al., 1985a,b; Prior et al., 1989; Weaver et al., 1992; Garcia and Hull, 1994; Lee and Chough, 2001; Xu et al., 2004; Li, 2005; Zhang et al., 2006; Warrick et al., 2008; Salles et al., 2008;
X. Huang et al. / Marine and Petroleum Geology 35 (2012) 190e200
Figure 1. Cross-section of flume experiment equipment. [Figure taken from Li, 2005].
Uroza and Steel, 2008), but none of the above studies looked at the effect of variations in the direction of these forcing processes. Furthermore, the autochthonous process of oversteepening of the parent deposit on the delta front can be another potential cause of instability within a sedimentary deposit (Banerjee, 1966; Prior et al., 1986, 1987; Li, 2005; Zhang et al., 2006; Girardclos et al., 2007). However, most published models of turbidites on the delta toe and prodelta have focused on the delta-slope morphology, with slopes varying from 6 to 35 (FoÈrstner et al., 1968; Sturm and Matter, 1972; Sturm, 1976; Sturm and Matter, 1978; Bogen, 1983; Kostaschuk and McMann, 1983; Carter, 1988; Corner et al., 1990; Meixner, 1991; Siegenthaler and Sturm, 1991; Jansa, 1991; Mosher et al., 1994). There are also limited data concerning the localised preexisting topographic slope angle in the area where fan-delta systems have developed (Shepard, 1963; Shepard and Dill, 1966), and the effect of preexisting topographic slope angle on natural delta systems is poorly constrained (Lajoie, 1979; Macdonald, 1986). Numerical modelling of ‘fluxoturbidite’ initiation may provide insight into variations of internal architecture of these deposits, and help understand the evolution of ‘fluxoturbidite’ deposition and the location of these potential hydrocarbon reservoir rocks. The aim of this study is to show the application of stratigraphic forward modelling and in particular the ability of the Sedsim computer program (Tetzlaff and Harbaugh, 1989; Griffiths and Paraschivoiu, 1998) to capture some of the significant features of sand avalanches at flume tank scale, and investigate potential causal processes such as topographic slope, water level fluctuation and wave action. Reference examples of the modelling potential and capabilities are illustrated by numerically replicating physical flume experiments. Numerical results from Sedsim models are compared with the experimental results from the physical flume tank in order to validate the model capabilities. Eventually, three of the many possible causal mechanisms of ‘fluxoturbidity’ are investigated using numerical stratigraphic forward modelling. 2. The Sedsim program Sedsim is a three-dimensional numerical stratigraphic forward model that simulates a wide variety of depositional processes (e.g., sediment erosion, transport, and deposition) both on geological and engineering time scales. It is a process model which uses a marker-in-cell approach presented by Tetzlaff and Harbaugh (1989) to overcome the limitations caused by the difficulty of solving the full NaviereStokes equations and the continuity
191
equation. This approach combines the attributes of Eulerian and Lagrangian equations and allows Sedsim to successfully simulate complex depositional systems and predict the erosion, transport and deposition of sediment in three spatial dimensions over time. A series of Sedsim modules simulating sediment transport, subsidence, flexural isostatic loading, compaction, slope failure and other processes, are represented by Tetzlaff and Harbaugh (1989), Griffiths and Paraschivoiu (1998), Griffiths et al. (2001), Liang et al. (2005), Li et al. (2004, 2006, 2007, 2008) and Salles et al. (2010). Sedsim is controlled by a number of input parameters, for example, relative sea level/base-level curve, initial topography/ bathymetry, tectonic movement, sediment input rates etc. Sedsim enables a wide variety of dynamic processes to be simulated quickly and thus evaluation of the impact of a range of different parameters can be analysed in a short period of time. Since 1994 Sedsim has been widely applied in hydrocarbon exploration and prediction (e.g., Griffiths and Paraschivoiu, 1998; Griffiths et al., 2001), and numerous researchers have adopted Sedsim to simulate and evaluate a variety of depositional systems. 3. Physical tank experiments The flume experiments (Li, 2005; Zhang et al., 2006) were conducted using a 5 m long by 2 m wide and 1 m deep flume (Fig. 1). The initial ramp was 0.6 m high 3.5 m long and 2 m wide, with a slope of 0.16 (9 ). The input parameters of the physical experiments are shown in Table 1. Observed depositional slopes in rift basins in China range from 3 to 20 (Lai and Zhou, 1994; Liu et al., 1995; Feng and Li, 2001; Jin et al., 2003; Du et al., 2005). The sediment released into the tank had a grain-size range within that observed in modern (e.g., Girardclos et al., 2007) and ancient delta systems (e.g., Rao et al., 2004; Yin et al., 2006; Yan et al., 2004), and was carried as a suspended load that consisted of 64.7% clay and 35.3% silt and fine sand. The sediment input was not central in the tank, but was located 60 cm from the glass wall and 50 cm from the steep edge of the tank. The total volume of sediment supply was adjusted so that the delta system was appropriate size for the specific flume tank, as was the water discharge rate. Before the experiments, the ramp and flat basin were covered by a smoothed initial 3 cm layer of sandy sediments. Two experimental runs were carried out. Run 1 investigated ‘fluxoturbidite’ production without external trigger and ran for 1446 min using a water level that rose uniformly from 0.35 m to 0.4 m during the first 230 min to build an initial delta deposit and was unchanged during the last 1216 min (Fig. 2). Run 2 investigated the effect of waves using the result from Run 1 as the initial topography. The waves had an 8 cm height and 4 min period representing moderate wave conditions. They were created by the up and down movements of a container (located at the centre point at the end of tank opposite the sediment supply) in the water (Fig. 1). The parameters of the two runs in this study are listed in Table 1. 4. Sedsim numerical simulations and results There were two goals for the Sedsim numerical simulations. The first was to verify that a numerical simulation could replicate the
Table 1 Input parameters of the flume experiments. (Table taken from Zhang et al., 2006). Experiments
Run 1 Run 2
Trigger
None Wave
Time (min)
1446 1446
Water depth (cm)
35e40 40
Water discharge rate (ml/s)
41.7 41.7
Total sediment (kg)
730 730
Sediment composition (%)
Slope
Coarse
Medium
Fine
Finest
Clay
0 0
5.4 5.4
29.9 29.9
30.4 30.4
34.3 34.3
9 9
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Figure 2. Diagram showing the topography with pre-deposit and water level curve used in Sedsim modelling. The initial ramp is 0.6 m high 3.5 m long and 2 m wide, and the predeposit layer is 3 cm thick (yellow for sandy sediments layer, brown for initial ramp) (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).
Table 2 Parameters and parameter values used in the Sedsim Model. Experiments
Vx (m/s)
Vy (m/s)
Run 1 Experiments Run 2
0 0.0417 Tank time (min) 4
Water discharge rate (m3/s)
Concentration (kg/m3)
1 mm
0.0000417 Waves height (m) 0.08
201.775
0 5.4 Wave base (m) 0.04
physical tank experiments in all essential details (i.e. scale, input parameters and output geometries and grain-size distributions. The second, once we were sure that the physical experiments could be numerically replicated, was to extend the range of the experiments to investigate parameters that were not physically investigated due to lack of time or equipment. The main aim of both the physical and numerical flume tank experiments was to investigate how much the behaviour of the sediment slumping and redeposition leading to ‘fluxoturbidites’ is sensitive to changes in various forcing conditions. Initially we carried out a series of simulations using the input parameters shown in Table 2, based on the parameters reported by Li (2005) and Zhang et al. (2006) at physical flume tank scale (Table 1). The topographic surface with pre-deposit, over which sediment was transported, deposited and eroded, was reproduced at the same scale (Fig. 2). Sediment was introduced to the model in the same location of the tank with volumes of sediment as listed in Table 1. In Run 1, the simulation time ran from 0 to 1446 min with a display interval of 20 min. The spatial resolution was 0.1 m giving a grid of 50 by 20 cells. Sediments supplied to the tank had grain diameters of 1 mm, 0.3 mm, 0.03 mm, and 0.002 mm, with a density of 2650 kg m3. The angle of repose for each grain size was set according to the generalized relationship between sediment grainsize and angle of internal friction (Kirkby, 1987; Nemec, 1990b; Orton and Reading, 1993). Studies of modern and ancient slope
Table 3 Parameters and parameter values used in Sedsim Model. Process
Run
Physical reference (Run 1)
Range
Slope angle Water level change Process Wave action
3e15 16e20 Run 21e30 31e39 40e45
9 0.4 m Reference (Run 2) Wave height: 0.08 m Wave base: 0.04 m Wave direction: 0
7 e20 0.35e0.4 m Range 0.04e0.4 m 0.02e0.1 m 10 e60
0.3 mm
0.03 mm
0.002 mm
29.9 64.7 Wave direction 0
failures indicated that the critical value for fine sandy deposits is up to 30 (Orton and Reading, 1993), to between 25 and 10 within sand- and silt- dominated deposits and clay- dominated deposits, respectively (Kirkby, 1987). Sediment failure can occur due to the autochthonous process of oversteepening. For Run 2, the Sedsim wave module was included, which generated waves uniformly southwards from the northern edge with the same amplitude and period as the physical model (Li, 2005; Zhang et al., 2006). The initial topography for Run 2 was the final surface from Run 1. In the following, three different series of numerical simulations have been run with a variety of parameters giving different initial conditions for the detachment (e.g., topographic slope, water level fluctuation and wave actions, see Table 3).
4.1. The reference models The numerical model predictions for ‘fluxoturbidite’ deposition are plotted together with the physical experimental results in Figures 3e6. In Run 1, during the time from the beginning of the run, the simulation results as shown in Figure 3 indicate that the delta system initially aggrades (A), during which sands are deposited mainly on the delta plain, whereas silt and clay are transported to the delta front; slumping begins at (B), and continues (CeD), producing numerous slump scars around the delta front, gullies in the prodelta (E, F), and ‘fluxoturbidite’ terraces below the delta front (Fig. 6), despite the constant water level. The resultant morphology from the simulation (Fig. 3DeF) agrees with the observed physical experimental result (Fig. 4) both in position and form. The length and width of delta plain obtained are of the same order as those observed in the physical experimental data (a length of 1.5 m and width of 2 m are derived from the numerical model as compared to 1.4 m in length and 2 m in width as observed in the physical experiment). The length of delta front and prodelta also compare well with the experimental data (30 cm for 35 cm, 160 cm
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Figure 3. Simulation of the ‘fluxoturbidite’ systems in the delta front without trigger mechanism from the beginning of the run using Sedsim. (A), (B), (C) and (D) show composition of deposits, with colours denoting different grain sizes (yellow for fine sand, green for silt and grey for clay). (E) Shows an oblique view with colours representing locations (green for delta plain, yellow for foreslope and purple for prodelta). (F) Shows surface representing the final morphology. Vertical exaggeration of (E) and (F) is 4 times horizontal. See text for discussion (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).
for 175 cm obtained, respectively). Figure 3D also shows a greater distance of 3.9 m clay sediments with ligulate shape, which are perpendicular to the shoreline and extent to the slope break. In comparison, the physical experiment yields a maximum transport distance of 4 m. The results shown in Figure 5 indicate that the features of multiple foreset deposits with compaction and depression due to the diversion of channels seen in the physical experiment (Fig. 5B) are captured well by the numerical model (Fig. 5A). Furthermore, the maximum thickness and slope angle of the silt and sand dominated delta front (Fig. 5A), match the results shown in Figure 5B (maximum thickness of 12.75 cm for 13 cm observed and slope angle of 27.5 for 27 observed). The ‘fluxoturbidite’ deposits in the prodelta (Fig. 6) are in accordance with the deposits observed in the physical experiment (Fig. 4). However, some load structures in the physical experiment, such as pillow structures and flame structures in liquefied zone (Fig. 5B), cannot be produced directly in the numerical forward model (Fig. 5A) as the height of these structures is small with
respect to the flow depth and they have an area less than the simulation grid dimension. Nevertheless, over the same time scale, and allowing for aliasing effects of the grid size used, the simulation represents a reasonable recreation of sedimentary deposition in term of depocenter location, geometry and thickness. One feature to note concerning the difference between the physical and numerical experiments is the elapsed time for the experiments. Run 1 took less than 6 minutes of elapsed computer time to replicate the 1446 min of physical experimental time. In Run 2 the result of numerical simulation of wave action is consistent with the observed experimental results. The delta system is characterized by four obvious terraces (Figs. 7 and 8) including a delta plain unaffected by wave shadow, the liquefied zone in the delta front, the prodelta where most sandy sediment resettled, and the deep-water basin reached by some muddy sediment. Their lengths are close to those observed in the physical experiments (1.3 m for 1.2 m, 0.2 m for 0.3 m, 1.5 m for 1.5 m, and 1.5 m for 1.4 m observed, respectively). Figure 9 shows that the
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Figure 6. Perspective fence display of sections showing ‘fluxoturbidity’ deposits, which are denoted by arrows, in front of the delta front showing Sedsim predicted results. Vertical exaggeration is 4 times horizontal.
Figure 4. Sketch of the ‘fluxoturbidite’ systems in front of the delta front without trigger mechanism in flume tank. See text for discussion. From Li (2005).
theoretical value of the maximum wave base agrees with the experimental value. However, some features of the prodelta in the numerical model do not match the physical experimental results. The most
noteworthy difference is that the maximum transport distance between ‘fluxoturbidies’ and finger-shape sands is a little longer in the numerical model than in experiment. This discrepancy between the simulation and observation is probably due to the distribution of wave energy. The wave direction in the numerical simulation is a linear wave field from south to north whereas in the physical experiment the waves were created by a point source with energy propagated radially (Li, 2005; Zhang et al., 2006). Despite this minor discrepancy, the numerical simulation has captured the
Figure 5. (A) Cross section, which is perpendicular to shore compares with (B) flume experiment result which from Li (2005). , , , ¼ multiple foreset deposits. Colours represent range of grain sizes in Figure 3. See text for discussion (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).
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Figure 7. Simulation of the distribution of foreset deposition with waves using Sedsim modelling. (A), (B), (C) and (D) show composition of deposits at wateresediment interface. Colours represent range of grain sizes in Figure 3. (E) Shows surface representing the final morphology. The maximum transport distance between ‘fluxoturbidites’ and finger-shape sands is approximately 60 cm. Vertical exaggeration of (E) is 2 times horizontal. See text for discussion (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).
sediment movement, geometry and ‘fluxoturbidite’ distribution. Run 2 took 1 second of elapsed computer time to replicate the 4 min of physical experimental time.
4.2. The influence of topographic slope on ‘fluxoturbidite’
Figure 8. The distribution of foresets deposition with wave. The maximum transport distance between ‘fluxoturbidies’ and finger-shape sands is about 55 cm. From Li (2005).
Using the same parameters as used in the Run 1 reference model described above, but varying topographic slopes from 7 to 20 , allowed the investigation of the possible influence of topographic slope on ‘fluxoturbidite’ deposition as a response to slumping. Figure 10 clearly shows the relationship between slope angle and slump occurrence, and indicates that there is an optimum window of topographic slopes within which slumping leading to ‘fluxoturbidites’ occurs. Below 9 and above 18 slumping does not occur or generate ‘fluxoturbidites’ under the experimental conditions. The silt- and sand dominated slope profiles with topography of 7 and 8 in this study have maximum delta slope angles of 22.5 and 24.5 . These slope angles agree with the result suggested by Kirkby (1987), who suggested that the critical slope for slumping to be initiated within loosely packed silt and fine sand is generally greater than 25 . That slumping does not occur on such steep slopes is possibly due to initial sediment bypass enabling silt and sand dominated sediments to settle directly at the toe of slope without slope failure. This result might explain the difference between ‘fluxoturbidies’ induced by steep topographic slopes, especially extremely precipitous slopes, and any other mechanism (e.g., Bogen, 1983; Kostaschuk and McMann, 1983; Corner et al., 1990). As a consequence, the critical values of topographic slope for ‘fluxoturbidite’ deposition via slumping lie between 9 and 18 . It remains to be tested whether or not this gradient window is also present at full scale.
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Figure 9. (A), perspective fence of liquefied zone (Pseudo-seismic) with Sedsim modelling, compares with (B) flume experiment result which was from Li (2005). The maximum wave base is 4 cm both in simulation and experiment result.
Figure 10. Relation between topographic slope activity and slumping.
4.3. The influence of relative water level change on ‘fluxoturbidites’ What happens to simulated ‘fluxoturbidite’ sand thicknesses with water level changes? There are no water level oscillations in the reference model, the thicknesses slumped over time as shown
in Figure 11 show that slump occurrence is infrequent without a forcing event. Figures 12e14 show the thicknesses slumped over time for two model runs with a wave amplitude of 0.05 m and a period of eustatic water level oscillation ranging from 1.0 T (T ¼ 834 min) to
Figure 11. Thickness slumped from the standard reference model, plotted against elapsed model time and alongside the water level curve.
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Figure 12. The thickness slumped through time with an amplitude 0.05 m and eustatic period 1.0T (T ¼ 834 min).
Figure 13. The thickness slumped through time with an amplitude 0.05 m and eustatic period 0.5T (T ¼ 417 min).
0.5 T. The results reveal that in both runs, an increase of slumping resulting in ‘fluxoturbidity’ occurs during relative water level fall, whereas little slumping occurs when relative water level rose. Figure 13 also indicates that it is easier to initiate slumping prior to the second relative water level fall than the first. By the time of the first rise, steeper slopes had been established because of delta progradation so that the slumping was more readily initiated. A rapid increase in the frequency of slumping events is observed as the rate of relative water level fall increases (Fig. 14). Possibly because of the rapid rate of fall, sediments have less time to stabilise, and thus slumping occurs frequently. Figure 15 demonstrates that the amplitude and frequency of relative water level oscillations can have significant effects on the change of total sand thickness slumped. For the same amplitude the total sand thickness slumped is always higher with high frequency than with low frequency. Although the results of no- and lowamplitude models suggest that the slumping can happen without relative water level change, the incremental total sand thickness slumped is proportional to amplitude of water level change.
Consequently, the numerical analysis indicates that, at least under these tank conditions, the amplitude and frequency of water level oscillation are the most important parameters influencing ‘fluxoturbidite’ thickness. High amplitude and high frequency of water level oscillation usually leads to frequent slumping which is one cause of ‘fluxoturbidites’. 4.4. The influence of wave action on ’fluxoturbidite’ production The Sedsim model simulations are performed with a range of wave parameters, including wave height, wave base and wave direction as presented in Table 1. Figures 16e18 illustrate that variation in wave parameters plays an important role in the geographic distribution of ‘fluxoturbidite’ deposition. It is observed that the area of the sediment reworked zone significantly enlarges and migrates shoreward with increasing wave height (Fig. 16), reworking the previous delta plain deposits. Unlike the wave height influence, the effect of changing wave-base tends to change the transport distance of reworked sediments
Figure 14. The thickness slumped through time with the rate of change of water level.
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Figure 15. The total thickness slumped plotted against the amplitude of water level oscillation. Results are taken from the 21 separate model runs.
Figure 16. Sandy deposit thickness reworked due to the change of wave height ((A) for 0.04 m, (B) for 0.08 m, and (C) for 0.4 m, respectively).
Figure 17. Sandy deposit thickness reworked due to the change of wave base. The wave base is 0.02 m in (A), 0.04 m in (B) and 0.2 m in (C).
Figure 18. The distribution of sandy deposit thickness reworked with different wave angles (0 in (A), 30 in (B) for, and 60 in (C), respectively).
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(Fig. 17), with deeper wave-base leading to greater transport distance. Figure 18 shows that the distribution of ‘fluxoturbidite’ deposits induced by wave action is significantly affected by wave direction. Sandy sediments redeposit asymmetrically around the prodelta with wave angles ranging from 0 to 60 , and become digitate farther from delta plain, possibly having a significant effect on lateral reservoir continuity. 5. Conclusions Using the Sedsim stratigraphic forward modelling program, we simulated and validated recent interpretations concerning ‘fluxoturbidite’ generation and distribution in a series of physical flume tank experiments. The following can be concluded from the investigation: (1) Initial topographic slope angle significantly influences the evolution of ‘fluxoturbidite’ deposits. There may exist an optimum window of initial topographic slopes for the production of slump-initiated ‘fluxoturbidites’. This window was between 9 and 18 for the particular grain size distribution used in these experiments. (2) Rates of relative water level change play an important role in governing the occurrence of slump-initiated ‘fluxoturbidity’. The generation of ‘fluxoturbidites’ is more sensitive to water level falls. The amplitude and frequency of water level oscillations appear to influence ‘fluxoturbidite’ thickness. (3) Wave action, and in particular the incident angle, may influence the location and reservoir continuity of sand avalanche/ ‘fluxoturbidite’ reservoirs. (4) The numerical simulations took around 1/261 the time of the physical experiments, provided easy access to the internal geometries and facies distributions, and enabled the effects of a greater range of parameters to be studied in a shorter time interval. Numerical simulation provided a convenient tool for investigating a wide range of dynamic processes resulting in ‘fluxoturbidite’ deposition, and shows how a numerical model may complement physical flume tank studies. Acknowledgements We would like to thank C. Li of SINOPEC Sheng Li Oilfield for giving the experiment dataset and we also deeply thank K. Liu and X. Qi of CSIRO Earth Science and Resource Engineering, and F. Li Western Australia Dept of Transport for their very useful reviews and comments for the improvement of the paper. References Andresen, A., Bjerrum, L., 1967. In: Rmhards, A.F. (Ed.), Slides in Subaqueous Slopes in Loose Sand and Silt. Marine Geotechnique Uinv I11Press, Urbana, Chicago, pp. 221e239. Banerjee, I., 1966. Turbidites in a glacial sequence: a study from the Talchir Formation, Raniganj Coalfield, India. J. Geol. 5 (74), 593e606. Bjerrum, L., 1971. Subaqueous slope failures in Norwegian fjords. Geotechnology, 1e8. Bogen, J., 1983. Morphology and sedimentology of deltas in fjord and fjord valley lakes. Sediment. Geol. 36, 245e267. Carter, R.M., 1988. Post-breakup stratigraphy of the Kaikoura Synthem, CretaceousCenozoic), continental margin, southeastern New Zealand. NZ J. Geol. Geophys. 31, 405e429. Chang, C.Y., 1991. Geological characteristics and distribution patterns of hydrocarbon deposits in the Bohai Bay Basin, east China. Mar. Petrol. Geol. 8, 98e106. Chen, D.X., Pang, X.Q., Jiang, Z.Z., Zeng, J.H., Qiu, N.S., Li, M.W., 2009. Reservoir characteristics and their effects on hydrocarbon accumulation in lacustrine turbidites in the Jiyang Super-depression, Bohai Bay Basin, China. Mar. Petrol. Geol. 26, 149e162.
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