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In many subsurface industrial applications, fluids are injected into or withdrawn from a geologic formation.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.

Improving our ability to estimate the parameters that control water and heat fluxes in the shallow subsurface is particularly important due to their strong control on recharge, evaporation and biogeochemical processes. The objectives of this study are to develop and test a new inversion scheme to simultaneously estimate subsurface hydrological, thermal and petrophysical parameters using hydrological, thermal and electrical resistivity tomography (ERT) data. The inversion scheme-which is based on a nonisothermal, multiphase hydrological model-provides the desired subsurface property estimates in high spatiotemporal resolution. A particularly novel aspect of the inversion scheme is the explicit incorporation of the dependence of the subsurface electrical resistivity on both moisture and temperature. The scheme was applied to synthetic case studies, as well as to real datasets that were autonomously collected at a biogeochemical field study site in Rifle, Colorado. At the Rifle site, the coupled hydrological-thermal-geophysical inversion approach well predicted the matric potential, temperature and apparent resistivity with the Nash-Sutcliffe efficiency criterion greater than 0.92. Synthetic studies found that neglecting the subsurface temperature variability, and its effect on the electrical resistivity in the hydrogeophysical inversion, may lead to an incorrect estimation of the hydrological parameters. The approach is expected to be especially useful for the increasing number of studies that are taking advantage of autonomously collected ERT and soil measurements to explore complex terrestrial system dynamics.

iTOUGH2 (http://www-esd.lbl.gov/iTOUGH2) is a computer program for parameter estimation, sensitivity analysis, and uncertainty propagation analysis [Finsterle, 2007a,b,c]. It is based on the TOUGH2 simulator for nonisothermal multiphase flow in porous and fractured media. Most of the features of the TOUGH2 forward model used in iTOUGH2 are documented in Pruess [1991], with some added features described in Appendix A of the iTOUGH2 Command Reference [Finsterle, 2007b]. One of the added capabilities is to include heterogeneity by assigning element-specific permeabilities or permeability modifiers in block ELEME, Columns 41-50, or in block INCON.1, Columns 31-40 (see Finsterle [2007b], Appendix A.4). This approach requires that the permeability (or permeability-modifier) fields be generated externally and mapped onto the TOUGH2 mesh using some preprocessing software. This manual describes the added capability to internally generate heterogeneous, random, spatially correlated property fields (in particular heterogeneous permeability fields), and to map these fields onto the TOUGH2 mesh. The geostatistical simulation routines integrated into iTOUGH2 are taken from the widely-used Geostatistical Software Library GSLIB [Deutsch and Journel, 1992]. For this purpose geostatistical simulation routines from the widely-used Geostatistical Software Library GSLIB [Deutsch and Journel, 1992] have been integrated into iTOUGH2. The following interpolation and conditional geostatistical simulation methods are implemented: (1) GSLIB subroutine KTB3D, which allows for simple kriging, ordinary kriging, and kriging with various trend models; (2) GSLIB subroutine SGSIM, which allows for sequential Gaussian simulations; and (3) GSLIB subroutine SISIM, which allows for sequential indicator simulations. These geostatistical methods the can be used to generate permeability or permeability-modifier fields in linear or log space, conditioned on available data. The conditioning points of the geo

In this study we compare the use of kinetic and equilibrium reaction models in the simulation of gas (methane) hydrate behavior in porous media. Our objective is to evaluate through numerical simulation the importance of employing kinetic versus equilibrium reaction models for predicting the response of hydrate-bearing systems to external stimuli, such as changes in pressure and temperature. Specifically, we (1) analyze and compare the responses simulated using both reaction models for natural gas production from hydrates in various settings and for the case of depressurization in a hydrate-bearing core during extraction; and (2) examine the sensitivity to factors such as initial hydrate saturation, hydrate reaction surface area, and numerical discretization. We find that for large-scale systems undergoing thermal stimulation and depressurization, the calculated responses for both reaction models are remarkably similar, though some differences are observed at early times. However, for modeling short-term processes, such as the rapid recovery of a hydrate-bearing core, kinetic limitations can be important, and neglecting them may lead to significant under-prediction of recoverable hydrate. The use of the equilibrium reaction model often appears to be justified and preferred for simulating the behavior of gas hydrates, given that the computational demands for the kinetic reaction model far exceed those for the equilibrium reaction model.

In this study we compare the use of kinetic and equilibrium reaction models in the simulation of gas (methane) hydrate behavior in porous media. Our objective is to evaluate through numerical simulation the importance of employing kinetic versus equilibrium reaction models for predicting the response of hydrate-bearing systems to external stimuli, such as changes in pressure and temperature. Specifically, we (1) analyze and compare the responses simulated using both reaction models for natural gas production from hydrates in various settings and for the case of depressurization in a hydrate-bearing core during extraction; and (2) examine the sensitivity to factors such as initial hydrate saturation, hydrate reaction surface area, and numerical discretization. We find that for large-scale systems undergoing thermal stimulation and depressurization, the calculated responses for both reaction models are remarkably similar, though some differences are observed at early times. However, for modeling short-term processes, such as the rapid recovery of a hydrate-bearing core, kinetic limitations can be important, and neglecting them may lead to significant under-prediction of recoverable hydrate. Assuming validity of the most accurate kinetic reaction model that is currently available, the use of the equilibrium reaction model often appears to be justified and preferred for simulating the behavior of gas hydrates, given that the computational demands for the kinetic reaction model far exceed those for the equilibrium reaction model.

We discuss the range of activities at Lawrence Berkeley National Laboratory in support of gas production from natural hydrates. Investigations of production from the various classes of hydrate deposits by numerical simulation indicate their significant promise as potential energy sources. Laboratory studies are coordinated with the numerical studies and are designed to address knowledge gaps that are important to the prediction of gas production. Our involvement in field tests is also briefly discussed.

We determined the composite thermal conductivity (ktheta) of a porous methanehydrate sample (composedof hydrate, water, and methan e gas) as a function of density using iTOUGH2. X-ray computed tomography (CT) was used to visualize and quantify the density changes that occurred during hydrate formation from granular ice. The composite thermal conductivity was estimated and validated by minimizing the differences between the observed and the predicted thermal response using history matching. The estimated density-dependent composite thermal conductivity ranged between 0.25 and 0.58 W/m/K.

In this study we compare the use of kinetic and equilibrium reaction models in the simulation of gas (methane) hydrates in porous media. Our objective is to evaluate through numerical simulation the importance of employing kinetic versus equilibrium reaction models for predicting the response of hydrate-bearing systems to external stimuli, such as changes in pressure and temperature. Specifically, we (1) analyze and compare the responses simulated using both reaction models for production in various geological settings and for the case of depressurization in a core during extraction; and (2) examine the sensitivity to factors such as initial hydrate saturation, hydrate reaction surface area, and numerical discretization. We find that for systems undergoing thermal stimulation and depressurization, the calculated responses for both reaction models are remarkably similar, though some differences are observed at early times. Given these observations, and since the computational demands for the kinetic reaction model far exceed those for the equilibrium reaction model, the use of the equilibrium reaction model often appears to be justified and preferred for simulating the behavior of gas hydrates.

Class 1 hydrate deposits are characterized by a Hy-drate-Bearing Layer (HBL) underlain by a two-phase zone involving mobile gas. Such deposits are further divided to Class 1W (involving water and hydrate in the HBL) and Class 1G (involving gas and hydrate in the HBL). In Class 2 deposits, a mobile water zone underlies the hydrate zone. Methane is the main hydrate-forming gas in natural accumulations. Using TOUGH-FX/HYDRATE to study the depressurization-induced gas production from such deposits, we determine that large volumes of gas could be readily produced at high rates for long times using conventional technology. Dissociation in Class 1W deposits proceeds in distinct stages, but is continuous in Class 1G deposits. Hydrates are shown to contribute significantly to the production rate (up to 65 percent and 75 percent in Class 1W and 1G, respectively) and to the cumulative volume of produced gas (up to 45 percent and 54 percent in Class 1W and 1G, respectively). Large volumes of hydrate-originating CH4 could be produced from Class 2 hydrates, but a relatively long lead time would be needed before gas production (which continuously increases over time) attains a substantial level. The permeability of the confining boundaries plays a significant role in gas production from Class 2 deposits. In general, long-term production is needed to realize the full potential of the very promising Class 1 and Class 2 hydrate deposits.

Reliable prediction of subsurface flow and contaminant transport depends on the accuracy with which the values and spatial distribution of process-relevant model parameters can be identified. Successful characterization methods for complex soil systems are based on (1) an adequate parameterization of the subsurface, capable of capturing both random and structured aspects of the heterogeneous system, and (2) site-specific data that are sufficiently sensitive to the processes of interest. We present a stochastic approach where the high-resolution imaging capability of geophysical methods is combined with the process-specific information obtained from the inversion of hydrological data. Geostatistical concepts are employed as a flexible means to describe and characterize subsurface structures. The key features of the proposed approach are (1) the joint inversion of geophysical and hydrological raw data, avoiding the intermediate step of creating a (non-unique and potentially biased) tomogram of geophysical properties, (2) the concurrent estimation of hydrological and petrophysical parameters in addition to (3) the determination of geostatistical parameters from the joint inversion of hydrological and geophysical data; this approach is fundamentally different from inference of geostatistical parameters from an analysis of spatially distributed property data. The approach has been implemented into the iTOUGH2 inversion code and is demonstrated for the joint use of synthetic time-lapse ground-penetrating radar (GPR) travel times and hydrological data collected during a simulated ponded infiltration experiment at a highly heterogeneous site.

Understanding fluid distribution and movement in the subsurface is critical for a variety of subsurface applications, such as remediation of environmental contaminants, sequestration of nuclear waste and CO2, intrusion of saline water into fresh water aquifers, and the production of oil and gas. It is well recognized that characterizing the properties that control fluids in the subsurface with the accuracy and spatial coverage needed to parameterize flow and transport models is challenging using conventional borehole data alone. Integration of conventional borehole data with more spatially extensive geophysical data (obtained from the surface, between boreholes, and from surface to boreholes) shows promise for providing quantitative information about subsurface properties and processes. Typically, estimation of subsurface properties involves a two-step procedure in which geophysical data are first inverted and then integrated with direct measurements and petrophysical relationship information to estimate hydrological parameters. However, errors inherent to geophysical data acquisition and inversion approaches and errors associated with petrophysical relationships can decrease the value of geophysical data in the estimation procedure. In this paper, we illustrate using two examples how joint inversion approaches, or simultaneous inversion of geophysical and hydrological data, offer great potential for overcoming some of these limitations.

We review recent progress made in the development and application of a method for jointly inverting time-lapse ground-penetrating radar (GPR) travel times and hydrological data to estimate field-scale soil hydraulic parameters. Our approach takes advantage of a wide range of cross-borehole GPR data acquisition configurations and hydrological measurement types, and accommodates uncertainty in the petrophysical function, which relates soil porosity, water saturation, and temperature to the effective dielectric constant. Through synthetic examples, we observe that realistic errors in the petrophysical function result in substantial errors in soil hydraulic parameter estimates, but such errors are minimized through simultaneous estimation of petrophysical parameters. We also discuss application of the method to large-scale field experiments involving the injection of water and heat. These examples demonstrate that the complimentary information contained in geophysical and hydrological data can be successfully extracted in a joint inversion approach. Moreover, since the generation of tomograms is not required, the amount of GPR data required for analyses is relatively low, and difficulties inherent to tomography methods are alleviated. Finally, the approach provides a means to capture the properties and system state of heterogeneous soil, both of which are crucial for assessing and predicting subsurface flow and contaminant transport. This work was supported in part by the U.S. Dept. of Energy under Contract No. DE-AC03-76SF00098.

Subsurface flow and contaminant transport processes are critically affected by the structure and heterogeneity of the subsurface as well as the related distribution of soil moisture. While geophysical methods (such as GPR) may provide high-resolution images of the subsurface, the relation between these images and parameters affecting flow and transport remains ambiguous. On the other hand, while hydrological data contain information about properties relevant to flow and transport, their spatial coverage and resolution are usually insufficient. To combine the strengths of both characterization methods, a joint geophysical-hydrological inversion approach was developed by coupling (a) an unsaturated flow simulator, (b) a forward model for calculating GPR travel times, and (c) a geostatistical simulation package, and linking them with nonlinear optimization methods. The approach is examined for a ponded infiltration test, in which time-lapse multiple-offset cross-borehole GPR travel times and hydrological data (flow rates and water content measurements) are used to estimate (a) soil hydraulic parameters that govern unsaturated flow, (b) petrophyscial parameters that relate the dielectric constant to water content, and (c) geostatistical parameters that characterize the soil structure. Focusing on the estimation of geostatistical measures (such as correlation length, anisotropy, and its orientation), we examine viable parameterizations of the highly variable (but structured) subsurface, evaluate model bias as well as estimation and prediction uncertainties, and discuss the advantages and limitations of joint inversion techniques for the characterization of soil properties and fluid distribution in the vadose zone. This work was supported in part by the U.S. Dept. of Energy under Contract No. DE-AC03-76SF00098.

A method is described for jointly using time-lapse multiple-offset cross-borehole ground-penetrating radar (GPR) travel time measurements and hydrological measurements to estimate field-scale soil hydraulic parameters and parameters of the petrophysical function, which relates soil porosity and water saturation to the effective dielectric constant. We build upon previous work to take advantage of a wide range of GPR data acquisition configurations and to accommodate uncertainty in the petrophysical function. Within the context of water injection experiments in the vadose zone, we test our inversion methodology with synthetic examples and apply it to field data. The synthetic examples show that while realistic errors in the petrophysical function cause substantial errors in the soil hydraulic parameter estimates, simultaneously estimating petrophysical parameters allows for these errors to be minimized. Additionally, we observe in some cases that inaccuracy in the GPR simulator causes systematic error in simulated travel times, making necessary the simultaneous estimation of a correction parameter. We also apply the method to a three-dimensional field setting using time-lapse GPR and neutron probe (NP) data sets collected during an infiltration experiment at the U.S. Department of Energy (DOE) Hanford site in Washington. We find that inclusion of GPR data in the inversion procedure allows for improved predictions of water content, compared to predictions made using NP data alone.

An approach that employs concepts from the pilot point method is used for estimating flow parameter distributions in the vadose zone with crosshole ground-penetrating radar (GPR) travel times along with hydrological data (e.g., point measurements of permeability, and water saturation values inferred from neutron probe data) collected during transient flow experiments. The methodology presented allows for estimation of the non-uniform permeability field (as well as porosity and additional uniform parameters describing the relative permeability and capillary pressure functions) and provides measures of parameter uncertainty. Various aspects of the approach, such as data collection configurations, are examined through a 2D synthetic model. In addition, inversion of data collected during an infiltration test at the Hanford field site allows for the strengths and limitations of the approach in a 3D setting to be explored.

Methods for estimating hydraulic properties are in great demand for modeling fluid flow and contaminant transport in the shallow subsurface. Estimates of flow parameters (such as permeability, porosity and parameters defining water retention curves) are typically obtained through the inversion of hydrogeological data (e.g., water saturation and capillary pressure measurements). However, ill-posedness and non-uniqueness often arise in such inverse problems making the solutions elusive. Incorporating additional types of data, such as from geophysical methods, is one way to improve the situation. Ground-penetrating radar (GPR) in particular has proven to be sensitive to subsurface fluid flow processes (e.g., changes in water saturation) though some uncertainty exists in interpreting GPR images. In the present work, an inverse technique is presented that uses time-lapsed GPR measurements along with transient hydrogeological data. Specifically, a maximum a posteriori (MAP) formulation, based on the Bayesian approach to parameter uncertainty, is chosen for its ability to incorporate prior information into the solution; this general framework can be simplified to other commonly used inverse problem formulations. Through a synthetic case study, numerical modeling of GPR and variably saturated flow allows for exploration of the benefits offered by inclusion of GPR data in estimation of flow parameters.

Inverse modeling is used to analyze the laboratory data and also determine the composite thermal conductivity of the porous methane hydrate sample as a function of density. The analysis has shown that the sample heterogeneity should be considered in thermal conductivity measurements of hydrate systems.