Title: |
Three-Dimensional Joint Bayesian Inversion of Hydrothermal Data Using Automatic Differentiation |
Authors: |
V. Rath, A. Wolf, M. Buecker |
Key Words: |
Bayesian inverse modeling, sensitivity, experimental design |
Conference: |
Stanford Geothermal Workshop |
Year: |
2006 |
Session: |
Modeling |
Language: |
English |
Paper Number: |
Rath |
File Size: |
317KB |
View File: |
|
We have developed a Bayesian inverse modeling tool for the joint inversion of hydraulic and thermal data. It is based on a well-known and well-tested forward modeling code, which solves the coupled steady state equations for heat and mass transfer. Because of the heat and pressure dependence of most petrophysical properties, this is a nonlinear problem. Different nonlinearities of petrophysical properties and pore-space models can be employed. The forward code was automatically differentiated to obtain partial derivative information. This information was used in several optimization schemes (Gauss-Newton, Quasi-Newton, Nonlinear Conjugate Gradients) to find the minimum of the Bayesian objective function. In addi-tion to the optimum model, the Bayesian approach supplies linear estimates of posterior covariances and related measures of uncertainty. We will present several instructive synthetic investigation demon-strating the power of the approach. Additionally, sensitivites and their use for the optimization of ex-perimental design are discussed.
Press the Back button in your browser, or search again.
Copyright 2006, Stanford Geothermal Program: Readers who download papers from this site should honor the copyright of the original authors and may not copy or distribute the work further without the permission of the original publisher.
Attend the nwxt Stanford Geothermal Workshop,
click here for details.