Simulation of Hydrothermal Processes around an ...

Simulation of Hydrothermal Processes around an Intrusion. Louise A. Fisher, Guillaume Duclaux, James S. Cleverley, Heather A. Sheldon Minerals Down Under Flagship, CSIRO, PO Box 1130, Bentley, WA 6102, Australia Abstract: A series of simulations were carried out to examine interactions and feedback between deformation, heat, chemical reactions and fluid flow in a hydrothermal system around a granite intrusion. Deformation-fluid flow and reactive transport simulation codes were used to model different aspects of the system with the results from one set of simulations informing the initial parameters of the other, providing a ‘soft-coupling’ approach. The development of a chemical alteration system is simulated and the results used to predict geophysical properties that can be used to test the impact of the altered rheology on stress and strain partitioning and fluid focusing during deformation. Keywords: Reactive Transport, FLAC3D, deformation, fluid flow

1 Introduction Patterns and rates of fluid flow in hydrothermal systems are an important control on the grade and distribution of mineralisation. Computer simulations can help us to understand and predict these patterns and the factors that control them. Fluid flow in the Earth’s crust is driven by the combined effects of deformation, topographic head, buoyancy (due to variations in both temperature and salinity), and chemical reactions that produce or consume fluid. The intrusion of a hot granite into cooler rocks triggers numerous fluid flow processes which drive the formation of a hydrothermal system (e.g. Hanson, 1995). How these processes interact, and which processes are the dominant controls on the scale of the system, can be explored using reactive transport and deformation-fluid flow simulation codes. At present we are not able to simulate all of the processes in a hydrothermal system in a fully-coupled manner. However, by taking a ‘soft-coupling’ approach, using rock properties and chemical zonation predicted from reactive transport modelling to inform the initial parameters of a deformation-fluid flow model, we can start to examine the interplay of these processes.

cases results from reactive transport simulations were used to inform the input parameters for subsequent deformation-fluid flow simulations. Flac3D is a Lagrangian finite difference code that can simulate fluid flow driven by the combined effects of deformation, topographic head, thermal buoyancy, and fluid production in three dimensions. The Reactive Transport (RT) code developed by the Predictive Mineral Discovery Cooperative Research Centre (pmd*CRC) consists of a Gibbs minimisation solver (WinGibbs; Shvarov and Bastrakov 1999) coupled with a partial differential equation (PDE) solver (originally Fastflo4 (Gross 2002), with later versions using EScript (Gross et al., 2007)) which solve the equations of fluid flow, heat and mass transport, and chemical reactions on a 2D or 3D finite element mesh. The RT code simulates incompressible single-phase flow in a fluid-saturated porous medium and assumes thermodynamic equilibrium between chemical species in the fluid and solid phases. Application of the RT code to predict geophysical signatures of alteration systems has been demonstrated by Chopping and Cleverley (2008). Their approach has been emulated here, by using density and chemical alteration patterns predicted by the RT code to provide input for pre- and post-alteration simulations of deformation-fluid flow-heat transport.

3 Development of Convection around a Granite

2 Numerical Simulation of Hydrothermal Systems To investigate the significance of different fluid flow drivers in the development of a hydrothermal system around a granite intrusion, a number of simple models were run using Flac3D version 3 (Itasca Consulting Group, 2005) to model coupled deformation, fluid flow and advective heat transport, and a reactive transport (RT) code to simulate coupled fluid flow, heat and mass transport and chemical reactions. Models were run using the same geometry in both codes, and in some Proceedings of the Tenth Biennial SGA Meeting, Townsville, 2009

Figure 1. Initial temperature conditions for RT granite model.

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A simple simulation of a hot granite intruded into cooler granite was tested using both Flac3D and the RT code. A 10km x 10km mesh with a 2km x 1.5km granite body at the centre was constructed. The top surface was defined as the earth’s surface with appropriate PT conditions, with the granite emplaced at 4km depth. The initial temperature of the granite was defined as 800oC and allowed to evolve while the surrounding rock has an initial temperature gradient of 30oC/km (Fig. 1). Both the intrusion and surrounding cooler granite were modelled with a fixed permeability of 10-16 m2 with the porosity of the granite at 10% and the surrounding rock having 5 % porosity. The permeability is assumed to be constant for the purpose of these preliminary models although in reality a decrease in permeability with depth would be expected. Evolution of a convection system was observed and alteration patterns were predicted by the simulation. A simple granitic composition was defined for both the intrusion and surrounding rock, comprising quartz-Kfeldspar-muscovite-magnetite-hematite and a 2 molal NaCl-KCl fluid was specified as the initial fluid within the intrusion. As the system evolved over 100,000 years a series of alteration zones developed within and above the intrusion (Fig. 2).

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Evaluating the Significance of Different Fluid Flow Processes in Driving a Hydrothermal System.

Factors such as permeability, thermal gradients and fluid viscosity that drive the development of convective cells around a hot granite are themselves interdependent on physiochemical conditions including pressure, temperature and fluid density. To test how these factors influence the evolution of a hydrothermal system a series of simulations were run in which the intruding granite was always at an initial temperature of 800oC but the surrounding rocks had PT gradients equating to different burial depths. An inert tracer was added to the fluid within the granite to track fluid movement, and streamlines were plotted to illustrate the developing flow fields (Fig. 3).

Figure 3. The different evolution of convecting systems with respect to (A) Temperature and (B) Maximum fluid velocity around a hot granite system at different simulated burial depths.

Figure 2. Reactive transport simulations predict the development of alteration zones within and above the granite. 856

Strong convective flow is observed for both simulations, but significant variations in fluid flow rates and the duration of the convection cells are noted as well as in the size of the convection cell and the degree of tracer dispersion. The duration of the system will be a controlling factor on the potential for a economic mineralising system to develop; prolonged flow and

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convection promoting concentration of metals.

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increased

scavenging

and

Simulating Deformation of Fresh and Altered Hydrothermal Systems

Using the methods outlined by Chopping and Cleverley (2008) the pre- and post-alteration properties can be calculated for this simulated granitic intrusion system (Fig.4.). These properties and other rheological properties estimated for the defined mineralogies were used to provide input parameters for a series of deformation-fluid flow simulations comparing the response of the unaltered system under contraction and extension with that of the altered system.

different densities and rheological parameters (i.e. cohesion, shear modulus, dilation angle, friction angle). Different bulk strain rates (from 10-13 to 10-15 s-1) were tested in contraction and extension. Different fluid flow patterns are observed between the static and deformed models (Fig. 5. left). Deformation starts to localise within the intrusion, as a response to the rheological input parameters and then concentrates at the top of the intrusion, where the major alteration zone is predicted in the RT models. In the case of the contraction models, shear strain localises along shallow dipping shear zones (Fig. 5 right) that propagate to the edge of the model after about 15 kyr (at a strain rate of 2e-14 s-1). The formation of shear zones would influence permeability and fluid flow, potentially localising mineral deposition. These feedbacks can be explored in further modelling work.

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Figure 4. Calculated rock densities (in g/cm3) under initial conditions (time = 0 years) and post-alteration (time = 100,000 years)

Future directions

This work has begun to answer some questions about the relative importance of various fluid driving forces in hydrothermal systems. These are preliminary results and further work is required to validate the method. The next step is to simulate a mineralising system and initial approaches will focus on gold mineralisation. Further development and testing of this soft-coupling approach will enable the incorporation of additional geological features, e.g. to study the role of faults in mineralising systems. The routine calculation of geophysical properties from reactive transport simulation results, including hyperspectral signals, will provide further value for exploration.

Acknowledgements The Reactive Transport code used for this work was developed by the pmd*CRC.

References

Figure 5. Comparison of integrated fluid flux (left) for both static (top) and under convergence (bottom) Flac3D models (time = 9570 years); and shear strain localisation at different steps of the model (right) (time = 9570 years –top – and 125000 years – bottom).

In the Flac3D simulations, changes in mechanical response between the granite intrusion and the host granite and alteration zones were tested by applying Proceedings of the Tenth Biennial SGA Meeting, Townsville, 2009

Chopping, R., and Cleverley, J.S., 2008, Flowpaths and Drivers: Creating forward geophysical models from reactive transport simulations: New Perspectives: The foundations and future of Australian explorations, Geoscience Australia Record 2009/09, p. 23-27. Gross, L., Cumming, B., Steube, K. and Weatherley, D., 2007, A Python module for PDE-based numerical modelling. In: Kågström, B., Elmroth, E., Dongarra, J. and Wasniewski, J. (Eds.), Applied Parallel Computing. State of the Art in Scientific Computing, p. 270-279. Gross, L., 2002. 'Fastflo4'. CSIRO Mathematical and Information Sciences Software. Hanson, R.B., 1995, The hydrodynamics of contact metamorphism: GSA Bulletin, v.107, p. 595-611. Itasca Consulting Group, 2002. Flac3D: Fast lagrangian analysis of continua in 3 dimensions. Itasca, Minneapolis. Shvarov, Y.V. and Bastrakov, E.N. (Eds). 1999. HCh: A software package for geochemical equilibrium modelling. User’s guide. Australian Geological Survey Organisation, Canberra, Australia.

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