MassFLOW-3D as a simulation tool for turbidity currents

Abstract

Turbidity currents are the most important mechanism for the dispersal and deposition of sand in the deep-sea setting and thus the main phenomenon leading to the formation of oil and gas reservoirs in deep water deposits. The flow characteristics of turbidity currents are difficult to observe and study from the modern environment and their experimental approximations in the laboratory are typically limited by scaling issues, unrealistic flume geometries and short durations. Computational fluid dynamic (CFD) analysis, realised as numerical simulations, has been developed to fill the gap between the small and large scale, integrating data from theory, nature and experiments. CFD can also shed light on flow parameters which are so far impossible to deduce from experimental and field studies, such as detailed density and turbulent kinematic energy distributions. The deterministic process modelling CFD software MassFLOW-3D™ has been developed and used successfully to construct a three-dimensional model for the simulation of turbidity currents. All principal hydraulic properties of the flow (e.g. velocity, density, sediment concentration, apparent viscosity, turbulence intensity and bottom shear stress) and its responses to topography can be monitored continuously in three dimensions over the whole duration of the turbidity current. In this paper, comparisons made between the numerical output of MassFLOW-3DTM and the physical experiments are presented. In addition, the code is used to model the spatial characteristics, velocity structure and deposits of high-density turbidity currents and the flow dynamics of low-density turbidity currents in a sinuous channel. The numerical simulations show close fit to experimental sandy turbidity current dynamics for flows with sediment concentrations up to 27%. However, despite this initial success, on-going customisation and validation of these models, together with implementation of improved subroutines aimed at sediment transport and deposition, is essential in improving the computational code and our understanding of the natural phenomena.