Observation Types Accepted by iTOUGH2

Input parameters for the TOUGH2 code can be estimated based on any type of sensitive data for which a corresponding TOUGH2 output is calculated. A list of observation types currently implemented in iTOUGH2 is shown below. Furthermore, an interface routine is provided in which users can specify their own observation types. This option is especially useful for defining arbitrary cost functions for the optimization of groundwater management problems.

Each observation refers to one or more grid blocks, connections or sink/source terms in the TOUGH2 model, or is related to the entire model domain. Time-dependent or steady-state data can be provided as lists in an arbitrary format. Measurement uncertainties can be specified for each data set or individually for each data point.

The following is a partial list of available observation types (see the Command Index for a complete list):

  • Pressure
    • Gas pressure
    • Liquid pressure
    • NAPL pressure
    • Capillary pressure
  • Flow rates
    • Total fluid flow rate
    • Liquid flow rate
    • Gas flow rate
    • NAPL flow rate
  • Saturation or phase content of a given phase
  • Concentration or mass fraction or mole fraction of a given component in a given phase
  • Temperature
  • Pressure drawdown with respect to a reference pressure
  • Flowing enthalpy
  • Relative humidity
  • Injection or production rate of a given phase in a well
  • Cumulative injection or production rate of a given phase in a well
  • First or second spatial moment of a given phase or component in the model
  • Total mass or change of mass of a given phase or component in the model
  • Total volume or change of volume of a given phase in the model
  • Secondary parameters (relative permeability, viscosity, density, specific enthalpy)
  • User-specified observation types
  • Prior information


Prior information:

A special type of observations are measurements or prior knowledge of the parameters that are to be estimated by inverse modeling. Adding prior information to the set of observations is a way of incorporating measured parameter values (e.g., porosity and permeability measurements from core samples). Prior information can also be used for regularization purposes, i.e., to make the inverse problem well-posed. iTOUGH2 also allows regularization baed on the difference between estimated parameter values.