Imaging of a fluid injection process using geophysical data - A didactic example

In many subsurface industrial applications, fluids are injected into or withdrawn from a geologic formation. It is of practical interest to quantify precisely where, when, and by how much the injected fluid alters the state of the subsurface. Routine geophysical monitoring of such processes attempts to image the way that geophysical properties, such as seismic velocities or electrical conductivity, change through time and space and to then make qualitative inferences as to where the injected fluid has migrated. The more rigorous formulation of the time-lapse geophysical inverse problem forecasts how the subsurface evolves during the course of a fluid-injection application. Using time-lapse geophysical signals as the data to be matched, the model unknowns to be estimated are the multiphysics forward-modeling parameters controlling the fluid-injection process. Properly reproducing the geophysical signature of the flow process, subsequent simulations can predict the fluid migration and alteration in the subsurface. The dynamic nature of fluid-injection processes renders imaging problems more complex than conventional geophysical imaging for static targets. This work intents to clarify the related hydrogeophysical parameter estimation concepts

Advanced Vadose Zone Simulations Using TOUGH

The vadose zone can be characterized as a complex subsurface system in which intricate physical and biogeochemical processes occur in response to a variety of natural forcings and human activities. This makes it difficult to describe, understand, and predict the behavior of this specific subsurface system. The TOUGH nonisothermal multiphase flow simulators are well-suited to perform advanced vadose zone studies. The conceptual models underlying the TOUGH simulators are capable of representing features specific to the vadose zone, and of addressing a variety of coupled phenomena. Moreover, the simulators are integrated into software tools that enable advanced data analysis, optimization, and system-level modeling. We discuss fundamental and computational challenges in simulating vadose zone processes, review recent advances in modeling such systems, and demonstrate some capabilities of the TOUGH suite of codes using illustrative examples

Seepage Calibration Model and Seepage Testing Data

The purpose of this Model Report is to document the Seepage Calibration Model (SCM). The SCM was developed (1) to establish the conceptual basis for the Seepage Model for Performance Assessment (SMPA), and (2) to derive seepage-relevant, model-related parameters and their distributions for use in the SMPA and seepage abstraction in support of the Total System Performance Assessment for License Application (TSPA-LA). This Model Report has been revised in response to a comprehensive, regulatory-focused evaluation performed by the Regulatory Integration Team [''Technical Work Plan for: Regulatory Integration Evaluation of Analysis and Model Reports Supporting the TSPA-LA'' (BSC 2004 [DIRS 169653])]. The SCM is intended to be used only within this Model Report for the estimation of seepage-relevant parameters through calibration of the model against seepage-rate data from liquid-release tests performed in several niches along the Exploratory Studies Facility (ESF) Main Drift and in the Cross-Drift. The SCM does not predict seepage into waste emplacement drifts under thermal or ambient conditions. Seepage predictions for waste emplacement drifts under ambient conditions will be performed with the SMPA [''Seepage Model for PA Including Drift Collapse'' (BSC 2004 [DIRS 167652])], which inherits the conceptual basis and model-related parameters from the SCM. Seepage during the thermal period is examined separately in the Thermal Hydrologic (TH) Seepage Model [see ''Drift-Scale Coupled Processes (DST and TH Seepage) Models'' (BSC 2004 [DIRS 170338])]. The scope of this work is (1) to evaluate seepage rates measured during liquid-release experiments performed in several niches in the Exploratory Studies Facility (ESF) and in the Cross-Drift, which was excavated for enhanced characterization of the repository block (ECRB); (2) to evaluate air-permeability data measured in boreholes above the niches and the Cross-Drift to obtain the permeability structure for the seepage model; (3) to use inverse modeling to calibrate the SCM and to estimate seepage-relevant, model-related parameters on the drift scale; (4) to estimate the epistemic uncertainty of the derived parameters, based on the goodness-of-fit to the observed data and the sensitivity of calculated seepage with respect to the parameters of interest; (5) to characterize the aleatory uncertainty of the parameters as a result of spatial variability; (6) to evaluate prediction uncertainty based on linear uncertainty-propagation analyses and Monte Carlo simulations; (7) to validate the SCM during model development, and validate the SCM using the post-development activities outlined in the Technical Work Plan (TWP); (8) to provide the technical basis for the resolution of unconfirmed issues previously labeled ''to be verified'' (TBV); and (9) to provide the technical basis for screening of certain seepage-related features, events, and processes (FEPs)

ITOUGH2 sample problems

This report contains a collection of ITOUGH2 sample problems. It complements the ITOUGH2 User`s Guide [Finsterle, 1997a], and the ITOUGH2 Command Reference [Finsterle, 1997b]. 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 fractured and porous media [Preuss, 1987, 1991a]. The report ITOUGH2 User`s Guide [Finsterle, 1997a] describes the inverse modeling framework and provides the theoretical background. The report ITOUGH2 Command Reference [Finsterle, 1997b] contains the syntax of all ITOUGH2 commands. This report describes a variety of sample problems solved by ITOUGH2. Table 1.1 contains a short description of the seven sample problems discussed in this report. The TOUGH2 equation-of-state (EOS) module that needs to be linked to ITOUGH2 is also indicated. Each sample problem focuses on a few selected issues shown in Table 1.2. ITOUGH2 input features and the usage of program options are described. Furthermore, interpretations of selected inverse modeling results are given. Problem 1 is a multipart tutorial, describing basic ITOUGH2 input files for the main ITOUGH2 application modes; no interpretation of results is given. Problem 2 focuses on non-uniqueness, residual analysis, and correlation structure. Problem 3 illustrates a variety of parameter and observation types, and describes parameter selection strategies. Problem 4 compares the performance of minimization algorithms and discusses model identification. Problem 5 explains how to set up a combined inversion of steady-state and transient data. Problem 6 provides a detailed residual and error analysis. Finally, Problem 7 illustrates how the estimation of model-related parameters may help compensate for errors in that model

ITOUGH2 V3.2 verification and validation report

This report describes the Verification and Validation (V and V) test cases performed to qualify ITOUGH2 V3.2. ITOUGH2 V3.2 was installed in a directory {approximately}/itough2v3.2 on a SUN ULTRA 1 workstation under UNIX Solaris 2. Instructions for installing ITOUGH2 can be found in file read.me and the user`s manual. This report is structured as follows: for each functional requirement, the corresponding design is described, which may include the mathematical model implemented in ITOUGH2 V3.2, if appropriate. Next, the author discusses the test case or sequence of test cases performed to validate each requirement, followed by a description of the test results and their compliance with the acceptance criteria. ITOUGH2 simulates fluid flow in fractures

P-mode leakage and Lyman-α intensity

We present an observational test of the hypothesis that leaking p modes heat the solar chromosphere. The amplitude of the leaking p modes in magneto-acoustic portals is determined using MOTH and MDI data. We simulate the propagation of these modes into the chromosphere to determine the height where the wave energy is dissipated by shock waves. A statistical approach is then used to check if this heating process could account for the observed variability of the intensity in the Lyman-α emissio

Inverse modeling and forecasting for the exploitation of the Pauzhetsky geothermal field, Kamchatka, Russia

A three-dimensional numerical model of the Pauzhetsky geothermal field has been developed based on a conceptual hydrogeological model of the system. It extends over a 13.6-km2 area and includes three layers: (1) a base layer with inflow; (2) a geothermal reservoir; and (3) an upper layer with discharge and recharge/infiltration areas. Using the computer program iTOUGH2 (Finsterle, 2004), the model is calibrated to a total of 13,675 calibration points, combining natural-state and 1960-2006 exploitation data. The principal model parameters identified and estimated by inverse modeling include the fracture permeability and fracture porosity of the geothermal reservoir, the initial natural upflow rate, the base-layer porosity, and the permeabilities of the infiltration zones. Heat and mass balances derived from the calibrated model helped identify the sources of the geothermal reserves in the field. With the addition of five makeup wells, simulation forecasts for the 2007-2032 period predict a sustainable average steam production of 29 kg/s, which is sufficient to maintain the generation of 6.8 MWe at the Pauzhetsky power plant