Complexity in Carbonate Systems

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Complexity in Carbonate Systems

• Jon Hill1
• Andrew Curtis1
• Rachel Wood2
• Dan Tetzlaff3

1Univeristy of Edinburgh 2Schlumberger Cambridge
Research and University of Cambridge 3Schlumberger
Boston Research
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Carbonate Deposition

• There are known differences between siliciclastic
  and carbonate deposition
• In-situ production
• Internal vs. external controls
• Carbonates are less predictable why?
• Which processes control this unpredictability?
• Physicochemical vs. Biological

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Carbonate Complexity

• Presence of both internal and external forcings
  on carbonate production rates
• Internal forcings have feedback mechanisms
• E.g. Andros Island tidal flats (Rankey, 2002)
  fractal distribution of facies

Open Channelsand Ponds
Mangrove
Algal Marsh
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Complexity
Here, complexity means complicated and
unpredictable

• Previous work has indicated that carbonate
  deposition is complex
• Statistical properties (e.g. Wilkinson, et. al,
  1997)
• Modelling work (e.g. Burgess and Emery, 2005)
• Implications for stratigraphic interpretation

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Model Formulation

• Forward model, Carbonate GPM an extension of a
  siliciclastic model, GPM
• Model includes
• Erosion and transport
• Two carbonate types
• Carbonate production based on
• Carbonate supersaturation
• Light levels
• Wave energy
• Based on physical and chemical parameters only

Residence Time 0
Open sea water CaCO3 supersaturated
Residence Time 1-100 days
Hypothesis Does carbonate complexity require
biological controls?
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Model Input

• Input
• Sea level
• Starting topography

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Model Output

• Output is a 3D volume of sediment
• Timelines drawn every 5kyr

Reef
Lagoon
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Residence Time

• Residence time reacts to changes in the topography

Velocity Snapshot
Diversion of flow
Islands
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Cycles

• Cycles picked on points of rapid deepening of
  water
• Around 90 cycles were generated in 1Myr
• Each run produced different cycles
• Different Fischer plot
• Cannot correlate

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Water Depth
Different limiting depths
Rapid initial growth
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Power Spectrum
No dominant periodicity
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Conclusions

• A tiny difference of 1m in initial topography
  produces very different results
• The model generates autocycles
• Different in each run and cannot be correlated
• Average depth converges to different limit
• Power spectrum shows no structure
• No simple predictability
• Simple, physicochemical processes produce complex
  behaviour without biological controls