Stochastic simulation of two-phase flow in porous media: Immiscible non-wetting fluid invasion in groundwater.

摘要

Contaminated groundwater is a significant problem in the United States and around the world, and it promises to be so for many years to come. Some of the most difficult groundwater remediation sights contain a type of contaminant called dense non-aqueous phase liquids (DNAPLs). In the past decade, extensive efforts have been undertaken to understand the role that DNAPLs play in groundwater contamination. Thus, any successful effort to model the process by which these chemicals penetrate the subsurface should be helpful in guiding groundwater remediation efforts.; DNAPL infiltration into groundwater is a two-phase fluid flow in porous media problem, such as those of oil recovery and carbon dioxide sequestration. Many numerical models have been created to describe two-phase flow with some success, but most have significant shortcomings. Either they are very slow, even for small systems, or they are not able to model flow behavior for situations where different forces (e.g., viscous, capillary, gravity) are important. Also, the problem of scaling up from a microscopic scale to a macroscopic scale exists for all numeric and conceptual models.; To address these problems, a rule-based model was created for this work that describes all types of flow behavior and is fast enough to run large (1000 x 1000 nodes) microscale simulations in a reasonable time. Model simulations have been made on 100 x 100 node networks for many combinations of important fluid and porous medium properties. The simulation results have been used to relate the breakthrough saturation and fractal dimension of the invading fluid to well-known dimensionless parameters (i.e., viscosity ratio, capillary number, Bond number).; The results of this research may be used in helping predict the location and extent of underground DNAPL contamination. Using the relationships between the dimensionless numbers and saturation and fractal dimension should also assist in scaling up from microscopic to macroscopic flow situations. This knowledge will assist in the creation of the next phase of two-phase microscopic and macroscopic flow models.