Almost 4 million metric tons of CO 2 were injected at the In Salah CO 2 storage site between 2004 and 2011. Storage integrity at the site is provided by a 950-m-thick caprock that sits above the injection interval. This caprock consists of a number of low-permeability units that work together to limit vertical fluid migration. These are grouped into main caprock units, providing the primary seal, and lower caprock units, providing an additional buffer and some secondary storage capacity. Monitoring observations at the site indirectly suggest that pressure, and probably CO 2 , have migrated upward into the lower portion of the caprock. Although there are no indications that the overall storage integrity has been compromised, these observations raise interesting questions about the geomechanical behavior of the system. Several hypotheses have been put forward to explain the measured pressure, seismic, and surface deformation behavior. These include fault leakage, flow through preexisting fractures, and the possibility that injection pressures induced hydraulic fractures. This work evaluates these hypotheses in light of the available data. We suggest that the simplest and most likely explanation for the observations is that a portion of the lower caprock was hydrofractured, although interaction with preexisting fractures may have played a significant role. There are no indications, however, that the overall storage complex has been compromised, and several independent data sets demonstrate that CO 2 is contained in the confinement zone.carbon sequestration | geomechanics

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Abstract. A stochastic Bayesian approach for combining well logs and geophysical surveys for enhancing subsurface characterization is presented. The main challenge we face is in creating the bridge to link between ambiguously related geophysical surveys and well data. The second challenge is imposed by the disparity between the scale of the geophysical survey and the scale of the well logs. Our approach is intended to integrate and transform the well log data to a form where it can be updated by the geophysical survey, and this tends to be a convoluted process. Our approach starts with generating images of the lithology, conditional to well logs. Each lithology image is then used as the basis for generating a series of shaliness images, conditional to well log data. Shaliness images are converted to resistivity images using a site-specific petrophysical model relating between shaliness, resistivity, and lithology, to create the necessary interface with the cross-well resistivity survey. The lithology and resistivity images are then updated using cross-well electromagnetic resistivity surveys. We explored the limits of the approach through synthetic surveys of different resolutions and error levels, employing the relationships between the geophysical and hydrological attributes, which are weak, nonlinear, or both. The synthetic surveys closely mimic the conditions at the LLNL Superfund site. We show that the proposed stochastic Bayesian approach improves hydrogeological site characterization even when using low-resolution resistivity surveys. A few observations based on these studies are as follows: (1) No universal methods or petrophysical models are available for converting geophysical attributes to hydrogeological ones; (2) The most challenging problem is tying well-logging measurements to the geophysical surveys. This issue involves problems of scale disparity and inconsistencies in the methods of data acquisition and interpretation. The last problem can be demonstrated by the fact that resistivity at the Lawrence Livermore National Laboratory (LLNL) site, which we explore later in this paper, was measured along boreholes using several different tools, each characterized by a different support volume, sometimes leading to dramatically different results.The present paper investigates the use of geophysical data and surveys for mapping lithology and soil properties in the subsurface using a Bayesian approach [Copty and Rubin, 1995].

Abstract. A method for analyzing and interpreting travel times of solutes in heterogeneous aquifers is presented based on the peak concentration arrival times as measured at various samplers in the aquifer. The method allows separation of the effects of pore-scale dispersion from the effects of the large-scale aquifer heterogeneity. An analysis of data from the Cape Cod field experiment is presented, and we found that the values of the hydrogeological parameters inferred from the travel times match very well the values obtained from direct interpretation of cores. Analysis of the spatial correlations of the travel times also allows inference of spatial covariances of the log conductivity and the anisotropy ratio of the correlation lengths. Separate analyses of the travel times carried over planes at different travel distances show that the variance of the log conductivity increases with travel distance.

This paper presents a theoretical framework for deriving the moments of the concentration, based on the Lagrangian approach and using a stochastic framework, conditional to measurements of conductivity and hydraulic head. The method consists of deriving the spatial correlations between concentration and travel time and hydrologic variables such as the conductivity and the hydraulic head. These correlations allow the conditioning of the moments of the concentration on measurements. By conditioning the concentration the uncertainty associated with its estimation can be reduced substantially. Consequently, difficulties associated with estimation of the extent of contamination can be alleviated. The theoretical framework and derivations may also be used to condition the moments of the conductivity on tracer data such as concentrations, travel times, and displacements. An application of such an approach would require a configuration of sources and samplers. We show that measured concentration is inferior to travel time and displacements in terms of efficient conditioning. 853 854 EZZEDINE AND RUBIN: TRACER DATA AND AQUIFER CHARACTERIZATION m y -[-Y' (•), where m y --(Y(g)) and angle brackets denote the expected value operator, and H(g) = (H(g)) + H' (•). Hence Y' and H' represent the fluctuations of Y and H about their respective means. The hydraulic head is assumed to be at steady state. In this study we shall assume small variability of the log conductivity, and we shall use the linearized steady state flow equation in order to relate between Y' and H' and other hydrologeological variables [cf. Gelhar and Axness, 1983; Dagan, 1985]. Under these conditions, H' becomes a linear function of Y', and their cross covariance can be derived analytically [cf.