Uncertainty Assessment of Subsurface Flow by a Probability Density Function Method
Abstract/Contents
- Abstract
- Incomplete knowledge about the subsurface environment, such as the geologic structure and petrophysical properties, leads to uncertainty in the predictions of subsurface flow and transport. We assess the impact of permeability uncertainty on the flow behavior for tracer flow using the Probability Density Function (PDF) method. The problem can be treated in several ways. Monte Carlo simulation is the most straightforward method but it consumes considerable CPU-time. Statistical Moment Equation (SME) methods build and solve the equations of the statistical moments of interest. SME methods usually rely on the assumption of small log-permeability (lnK) variance, which limits their applicability. The PDF method is used in turbulence modeling, and we use it here to address the subsurface flow problem. A transport equation for the probability density function of the variables of interest, such as concentration, is derived. The resulting high-dimensional equation is then solved by a particle method. Particles are released with stochastic velocities and evolved according to a velocity model that is based upon a bivariate normal distribution. Finally, the mean concentration profile, hCi, is obtained by averaging the particle field. hCi fields from the PDF method are observed to match the MCS results for permeability fields with high variance (2 lnK of 4) and long correlation scales.
Description
| Type of resource | text |
|---|---|
| Date created | December 2007 |
Creators/Contributors
| Author | Zhou, Wentao |
|---|---|
| Primary advisor | Caers, Jef |
| Primary advisor | Tchelepi, Hamdi |
| Degree granting institution | Stanford University, Department of Energy Resources Engineering |
Subjects
| Subject | School of Earth Energy & Environmental Sciences |
|---|---|
| Genre | Thesis |
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Preferred citation
- Preferred Citation
- Zhou, Wentao. (2007). Uncertainty Assessment of Subsurface Flow by a Probability Density Function Method. Stanford Digital Repository. Available at: https://purl.stanford.edu/qy747qc1915
Collection
Master's Theses, Doerr School of Sustainability
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