1. Complexity in Carbonate Systems Jon Hill 1 Andrew Curtis 1 Rachel Wood 2 Dan Tetzlaff 3 1 Univeristy of Edinburgh 2 Schlumberger Cambridge Research and University of Cambridge 3 Schlumberger Boston Research

2. Jon Hill, Andrew Curtis, Rachel Wood, Dan Tetzlaff Slide 2 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

3. Jon Hill, Andrew Curtis, Rachel Wood, Dan Tetzlaff Slide 3 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 Algal Marsh Open Channels and Ponds Mangrove

4. Jon Hill, Andrew Curtis, Rachel Wood, Dan Tetzlaff Slide 4 Complexity 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 Here, complexity means complicated and unpredictable

5. Jon Hill, Andrew Curtis, Rachel Wood, Dan Tetzlaff Slide 5 Open sea water CaCO 3 supersaturated Residence Time = 0 Residence Time ~ 1-100 days Model Formulation 050100150200250 Residence Time in the Lagoon (days) 0 0.2 0.4 0.6 0.8 1 Percentage of Maximum Growth 00.20.40.60.81 0 5 10 15 20 25 30 35 40 45 50 Water Depth (m) Percentage of Maximum Growth 050010001500200025003000 0 0.2 0.4 0.6 0.8 1 Wave Power (W/m ) 2 Percentage of Maximum Growth 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 Hypothesis: Does carbonate complexity require biological controls?

6. Jon Hill, Andrew Curtis, Rachel Wood, Dan Tetzlaff Slide 6 Model Input Input: –Sea level –Starting topography

7. Jon Hill, Andrew Curtis, Rachel Wood, Dan Tetzlaff Slide 7 Model Output Output is a 3D volume of sediment Timelines drawn every 5kyr Reef Lagoon

8. Jon Hill, Andrew Curtis, Rachel Wood, Dan Tetzlaff Slide 8 Residence Time Residence time reacts to changes in the topography Area of high residence time Residence Time Islands Diversion of flow Velocity Snapshot

9. Jon Hill, Andrew Curtis, Rachel Wood, Dan Tetzlaff Slide 9 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 Note: linear sea level change

10. Jon Hill, Andrew Curtis, Rachel Wood, Dan Tetzlaff Slide 10 Water Depth Rapid initial growth Different limiting depths

11. Jon Hill, Andrew Curtis, Rachel Wood, Dan Tetzlaff Slide 11 Power Spectrum No dominant periodicity

12. Jon Hill, Andrew Curtis, Rachel Wood, Dan Tetzlaff Slide 12 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