Name
Abstract | ||
|---|---|---|
A new approach is introduced for turbidite modeling, leveraging the potential of computational fluid dynamics methods to simulate the flow processes that led to turbidite formation. The practical use of numerical flow simulation for the purpose of turbidite modeling so far is hindered by the need to specify parameters and initial flow conditions that are a priori unknown. The present study proposes a method to determine optimal simulation parameters via an automated optimization process. An iterative procedure matches deposit predictions from successive flow simulations against available localized reference data, as in practice may be obtained from well logs, and aims at convergence towards the best-fit scenario. The final result is a prediction of the entire deposit thickness and local grain size distribution. The optimization strategy is based on a derivative-free, surrogate-based technique. Direct numerical simulations are performed to compute the flow dynamics. A proof of concept is successfully conducted for the simple test case of a two-dimensional lock-exchange turbidity current. The optimization approach is demonstrated to accurately retrieve the initial conditions used in a reference calculation. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.cageo.2010.09.015 | Computers & Geosciences |
Keywords | Field | DocType |
available localized reference data,numerical flow simulation,automated optimization process,towards inverse modeling,inverse lock-exchange problem,turbidity current,turbidite modeling,flow inversion,flow dynamic,optimization approach,initial flow condition,optimization strategy,surrogate management framework,flow process,successive flow simulation,grain size distribution,inverse modeling,reference data,initial condition,proof of concept,direct numerical simulation | Reference data (financial markets),Convergence (routing),Data mining,Turbidity current,Simulation,Computer science,Flow (psychology),Algorithm,Proof of concept,Fluid dynamics,Inverse problem,Computational fluid dynamics | Journal |
Volume | Issue | ISSN |
37 | 4 | Computers and Geosciences |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| L. Lesshafft | 1 | 1 | 0.73 |
| Eckart Meiburg | 2 | 3 | 1.66 |
| Ben Kneller | 3 | 0 | 0.34 |
| Alison Marsden | 4 | 52 | 8.83 |