Lawrence Berkeley National Laboratory, Report LBNL-40040, 1999

iTOUGH2 User's Guide

Stefan Finsterle

Lawrence Berkeley National Laboratory, Earth Sciences Division
University of California, Berkeley, CA 94720

Summary. iTOUGH2 is a program for parameter estimation, sensitivity analysis, and uncertainty propagation analysis. It is based on the TOUGH2 simulator for non-isothermal multiphase flow in porous and fractured media [Pruess, 1987, 1991].

This report describes the inverse modeling framework and provides the theoretical background for the methodologies employed by iTOUGH2. Furthermore, it discusses the architecture of iTOUGH2 and contains instructions for code installation and execution. This manual supplements the iTOUGH2 Command Reference, which explains the syntax of all iTOUGH2 commands, and the report iTOUGH2 Sample Problems, which contains a collection of illustrative iTOUGH2 applications.

The key to a successful application of iTOUGH2 is (1) a good understanding of multiphase flow processes, (2) the ability to conceptualize the given flow and transport problem and to develop a corresponding TOUGH2 model, (3) detailed knowledge about the data used for calibration, (4) an understanding of parameter estimation theory and the correct interpretation of inverse modeling results, and (5) proficiency in using iTOUGH2 options. This report partly addresses issue (4).

This report is intended to introduce inverse modeling concepts for applications in multiphase flow and transport simulations. While inverse modeling can be discussed in the jargon of applied mathematics and mathematical statistics, this manual is tailored to the needs of engineers and scientists who are interested in calibrating TOUGH2 models against observed data.

The report is organized as follows. After an introductory discussion of inverse modeling issues (Chapter 1), each element involved in automatic model calibration is described in detail in Chapter 2. These elements include the parameter vector, the vector of observable variables, the stochastic model, the objective function, the minimization algorithm, convergence criteria, the residual and error analyses, and uncertainty propagation analysis. Each element is discussed from a theoretical viewpoint, and reference to the corresponding iTOUGH2 input and output will be made. A line-by-line discussion of a typical iTOUGH2 output file is given in Chapter 3. Chapters 4 and 5 contain information about code architecture as well as instructions for installing and running iTOUGH2.