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.