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We present a synthetic multi-scale, multi-physics dataset constructed from the Kimberlina 1.2 CO2 reservoir model based on a potential CO2 storage site in the Southern San Joaquin Basin of California. Among 300 models, one selected reservoir-simulation scenario produces hydrologic-state models at the onset and after 20 years of CO2 injection. Subsequently, these models were transformed into geophysical properties, including P- and S-wave seismic velocities, saturated density where the saturating fluid can be a combination of brine and supercritical CO2, and electrical resistivity using established empirical petrophysical relationships. From these 3D distributions of geophysical properties, we have generated synthetic time-lapse seismic, gravity and electromagnetic responses with acquisition geometries that mimic realistic monitoring surveys and are achievable in actual field situations. We have also created a series of synthetic well logs of CO2 saturation, acoustic velocity, density and induction resistivity in the injection well and three monitoring wells. These were constructed by combining the low-frequency trend of the geophysical models with the high-frequency variations of actual well logs collected at the potential storage site. In addition, to better calibrate our datasets, measurements of permeability and pore connectivity have been made on cores of Vedder Sandstone, which forms the primary reservoir unit. These measurements provide the range of scales in the otherwise synthetic dataset to be as close to a real-world situation as possible. This dataset consisting of the reservoir models, geophysical models, simulated time-lapse geophysical responses and well logs forms a multi-scale, multi-physics testbed for designing and testing geophysical CO2 monitoring systems as well as for imaging and characterization algorithms. The suite of numerical models and data have been made publicly available for downloading on the National Energy Technology Laboratory's (NETL) Energy Data Exchange (EDX) website.

The Snake River Plain (SRP) volcanic province overlies the track of the Yellowstone hotspot, a thermal anomaly that extends deep into the mantle. Most of the area is underlain by a basaltic volcanic province that overlies a mid-crustal intrusive complex, which in turn provides the long-term heat flux needed to sustain geothermal systems. Previous studies have identified several known geothermal resource areas within the SRP. For the geothermal study presented herein, our goals were to: (1) adapt the methodology of Play Fairway Analysis (PFA) for geothermal exploration to create a formal basis for its application to geothermal systems, (2) assemble relevant data for the SRP from publicly available and private sources, and (3) build a geothermal PFA model for the SRP and identify the most promising plays, using GIS-based software tools that are standard in the petroleum industry. The study focused on identifying three critical resource parameters for exploitable hydrothermal systems in the SRP: heat source, reservoir and recharge permeability, and cap or seal. Data included in the compilation for heat source were heat flow, distribution and ages of volcanic vents, groundwater temperatures, thermal springs and wells, helium isotope anomalies, and reservoir temperatures estimated using geothermometry. Reservoir and recharge permeability was inferred from the analysis of stress orientations and magnitudes, post-Miocene faults, and subsurface structural lineaments based on magnetics and gravity data. Data for cap or seal included the distribution of impermeable lake sediments and clay-seal associated with hydrothermal alteration below the regional aquifer. These data were used to compile Common Risk Segment maps for heat, permeability, and seal, which were combined to create a Composite Common Risk Segment map for all southern Idaho that reflects the risk associated with geothermal resource exploration and identifies favorable resource tracks. Our regional assessment indicated that undiscovered geothermal resources may be located in several areas of the SRP. Two of these areas, the western SRP and Camas Prairie, were selected for more detailed assessment, during which heat, permeability, and seal were evaluated using newly collected field data and smaller grid parameters to refine the location of potential resources. These higher resolution assessments illustrate the flexibility of our approach over a range of scales.

Play Fairway Analysis (PFA) in geothermal exploration originates from a systematic methodology developed within the petroleum industry and is based on a geologic, geophysical, and hydrologic framework of identified geothermal systems. We tailored this methodology to study the geothermal resource potential of the Snake River Plain and surrounding region, but it can be adapted to other geothermal resource settings. We adapted the PFA approach to geothermal resource exploration by cataloging the critical elements controlling exploitable hydrothermal systems, establishing risk matrices that evaluate these elements in terms of both probability of success and level of knowledge, and building a code-based ‘processing model’ to process results. A geographic information system was used to compile a range of different data types, which we refer to as elements (e.g., faults, vents, heat flow, etc.), with distinct characteristics and measures of confidence. Discontinuous discrete data (points, lines, or polygons) for each element were transformed into continuous interpretive 2D grid surfaces called evidence layers. Because different data types have varying uncertainties, most evidence layers have an accompanying confidence layer which reflects spatial variations in these uncertainties. Risk layers, as defined here, are the product of evidence and confidence layers, and are the building blocks used to construct Common Risk Segment (CRS) maps for heat, permeability, and seal, using a weighted sum for permeability and heat, but a different approach with seal. CRS maps quantify the variable risk associated with each of these critical components. In a final step, the three CRS maps were combined into a Composite Common Risk Segment (CCRS) map, using a modified weighted sum, for results that reveal favorable areas for geothermal exploration. Additional maps are also presented that do not mix contributions from evidence and confidence (to allow an isolated view of evidence and confidence), as well as maps that calculate favorability using the product of components instead of a weighted sum (to highlight where all components are present). Our approach helped to identify areas of high geothermal favorability in the western and central Snake River Plain during the first phase of study and helped identify more precise local drilling targets during the second phase of work. By identifying favorable areas, this methodology can help to reduce uncertainty in geothermal energy exploration and development.

Monitoring, verification, and accounting (MVA) are crucial to ensure safe and long-term geologic carbon storage. Seismic monitoring is a key MVA technique that utilizes seismic data to infer elastic properties of CO2-saturated rocks. Reliable accounting of CO2 in subsurface storage reservoirs and potential leakage zones requires an accurate rock physics model. However, the widely used CO2 rock physics model based on the conventional Biot-Gassmann equation can substantially underestimate the influence of CO2 saturation on seismic waves, leading to inaccurate accounting. We develop an accurate CO2 rock physics model by accounting for both effects of the stress dependence of seismic velocities in porous rocks and CO2 weakening on the rock framework. We validate our CO2 rock physics model using the Kimberlina-1.2 model (a previously proposed geologic carbon storage site in California) and create time-lapse elastic property models with our new rock physics method. We compare the results with those obtained using the conventional Biot-Gassmann equation. Our innovative approach produces larger changes in elastic properties than the Biot-Gassmann results. Using our CO2 rock physics model can replicate shear-wave speed reductions observed in the laboratory. Our rock physics model enhances the accuracy of time-lapse elastic-wave modeling and enables reliable CO2 accounting using seismic monitoring.

Multiphysics inversion exploits different types of geophysical data that often complement each other and aims to improve overall imaging resolution and reduce uncertainties in geophysical interpretation. Despite the advantages, traditional multiphysics inversion is challenging because it requires a large amount of computational time and intensive human interactions for preprocessing data and finding trade-off parameters. These issues make it nearly impossible for traditional multiphysics inversion to be applied as a real-time monitoring tool for geological carbon storage. In this paper, we present a deep learning (DL) multiphysics network for imaging CO2 saturation in real time. The multiphysics network consists of three encoders for analysing seismic, electromagnetic and gravity data and shares one decoder for combining imaging capabilities of the different geophysical data for better predicting CO2 saturation. The network is trained on pairs of CO2 label models and multiphysics data so that it can directly image CO2 saturation. We use the bootstrap aggregating method to enhance the imaging accuracy and estimate uncertainties associated with CO2 saturation images. Using realistic CO2 label models and multiphysics data derived from the Kimberlina CO2 storage model, we evaluate the performance of the deep learning multiphysics network and compare its imaging results to those from the deep learning single-physics networks. Our modelling experiments show that the deep learning multiphysics network for seismic, electromagnetic, and gravity data not only improves the imaging accuracy but also reduces uncertainties associated with CO2 saturation images. Our results also suggest that the deep learning multiphysics network for the non-seismic data (i.e., electromagnetic and gravity) can be used as an effective low-cost monitoring tool in between regular seismic monitoring.

Predictive reservoir modeling, even if present in the form of only basic hydrogeological model assumptions, is expected to accompany the majority of carbon capture and sequestration monitoring activities. It thus represents a source of prior information about the migration of injected fluids that can benefit geophysical survey planning and ensuing monitoring. Constraining the imaging of geophysical monitoring data with reservoir modeling is preferable over standalone geophysical imaging because of additional complementary hydrogeological information. However, fully coupled hydrogeophysical data inversion for flow-modeling parameters that control saturation predictions is an involved process. Within the context of three-dimensional electromagnetic (EM) inversion of data from borehole-to-surface layouts, we employ a "poor people's" alternative. The approach constrains geophysical inversion parameters through saturation predictions. The coupling is realized through spatially variable lower and upper parameter bounds that scale with gas saturation magnitudes, the latter provided by reservoir modeling. Enhancement of three-dimensional time-lapse plume EM imaging is demonstrated for simulated sequestration into a depleted gas reservoir.

In 2002 and 2003 a collaborative effort was undertaken between Lawrence Berkeley National Laboratory, Sandia National Laboratories, the U.S. Geological Survey (USGS) Menlo Park, the USGS Hawaiian Volcano Observatory, and Electromagnetic Instruments Inc. to study the Kīlauea volcano in Hawaii using the magnetotelluric (MT) technique. The work was motivated by a desire to improve understanding of the magma reservoirs and conduits within Kīlauea and the East and Southwest Rift zones, which has implications for understanding Kīlauea's plumbing system. An improved understanding of the rift zones has implications in understanding large-scale landslides that are generated in the Hilina Slump, which produce significant impacts on coastal communities. Up to eight stations operated simultaneously, with multiple remote reference sites, and data were processed using multi-station robust processing techniques. In total, data were acquired at 70 sites over the Southwest and East rift zones. Good to excellent quality data were obtained even in the harshest conditions, such as those encountered on the fresh lava flows of the East Rift Zone, where electrical contact resistances are on the order of 100 kΩ. A three-dimensional (3D) MT model study was done to guide interpretation of the observed MT measurements. Synthetic modeling demonstrates that conductive bodies in the upper 3 km can be spatially resolved where MT station sampling is good. Resistivity anomalies in the 3D inversions have a high degree of spatial correlation with previously published seismic velocity anomalies beneath Kīlauea. Melt fractions between 0.096 and 0.117 are calculated for the Kīlauea and Puʻuʻōʻō low resistivity anomalies, respectively.

For geologic carbon storage, the ability to detect secondary CO2 plumes—defined as those CO2 plumes accumulating outside the intended storage reservoir—is fundamental to preventing unexpected CO2 migration into groundwater resources and for risk and liability management. Understanding the sensitivity of various geophysical methods to secondary plumes is crucial for designing cost-effective monitoring schemes. We use several modeling scenarios to demonstrate the process of assessing sensitivities and detection thresholds of three primary geophysical techniques—surface seismic, borehole-to-surface electromagnetic (EM), and surface and borehole gravity—for early detection of secondary CO2 plumes in the post-injection phase. While seismic reflection methods are often considered in monitoring strategies to track the evolution of CO2 plumes, they are also the most expensive. Due to cost considerations, especially for long-term post-injection monitoring, other techniques complement seismic monitoring when designing an adaptive monitoring network. Borehole-to-surface EM or surface gravity surveys are feasible for time-lapse monitoring of deep secondary CO2 plumes. These surveys could be carried at intervals defined by site-specific conditions. If time-lapse EM and/or gravity surveys detect any signal responses beyond the expected change, it would trigger a need for the higher resolution seismic survey.

In this study, the performance of both surface and borehole time-lapse gravity monitoring to detect CO2 leakage from a carbon storage site is evaluated. Several hypothetical scenarios of CO2 migration in a leaky fault, and thief zones at different depths at the Kimberlina site (California, USA) constitute the basis of the approach. The CO2 displacement is simulated using the TOUGH2 simulator applied to a detailed geological model of the site. The gravity responses to these CO2 plumes are simulated using forward modeling with sensors at ground surface and in vertical boreholes. Results of inversion on one scenario are also presented. The surface-based gravity responses obtained for the different leakage scenarios demonstrate that leakage can be detected at the surface in all the scenarios but the time to detection is highly variable (10–40 years) and dependent on the detection threshold considered. Borehole measurements of the vertical component of gravity provide excellent constraints in depth when they are located in proximity of the density anomaly associated with the presence of CO2, thus discriminating multiple leaks in different thief zones. Joint inversion of surface and borehole data can bring valuable information of the occurrence of leakages and their importance by providing a reasonable estimate of mass of displaced fluids. This study demonstrates the importance of combining multiphase flow simulations with gravity modeling in order to define if and when gravity monitoring would be applicable at a given storage site.

The Tatun geothermal system, located in northern Taiwan, is hosted by the Plio-Pleistocene Tatun volcanic group, consisting dominantly of andesitic lavas, domes and pyroclastic flows. During the late 1960s and early 1970s, geologic mapping, geochemical sampling, geophysical surveys, and the drilling of numerous exploration and temperature gradient wells were conducted at Tatun under the leadership of the Mineral Research and Service Organization (MRSO), the predecessor to the geothermal group at the Industrial Technology Research Institute (ITRI). Deep (1–2 km) wells encountered commercial temperatures (200–300 °C) in the Matsao area, but these initial exploration efforts were discouraged by the presence of very acidic (pH < 3.5) and highly corrosive fluids. Numerous geoscience studies conducted over the past 15 years at Tatun have shown that the central portion of the system is dominated by corrosive volcanic fluids, as evidenced by abundant SO2 as well as HCl. More reduced gases are encountered on the flanks of the system, suggesting that water-rock interaction may have at least partly neutralized the volcanic-derived fluids on the margins of this geothermal system.

This report considers the application of number of different indirect (remote sensing) geophysical techniques for monitoring geologic sequestration of CO2 in brine-bearing formations. The time-lapse monitoring using seismic, gravity, electrical and electromagnetic techniques is described. Geophysical models used for numerical modeling are based on flow simulations of various CO2 sequestration scenarios. Different monitoring approaches are suitable for a site characterization prior to CO2 injection, for monitoring while injecting CO2, or for a post-injection stage. Basic principles of each technique are described. Workflows are described using various examples. Each of the techniques is sensitive to a different subsurface property. The seismic velocity depends on the bulk and shear modulus and density, the gravity response depends on density, and the electrical resistivity is sensitive to changes in formation properties such as porosity, pore fluid resistivity, and fluid saturation. For this reason these techniques are complementary to each other, and when used together they could provide improved characterization of the subsurface. In addition to individual techniques, incorporating an electrical resistance tomography (ERT) into DREAM (Designs for Risk Evaluation and Management) tool is also described.

The region surrounding the Modoc Plateau, encompassing parts of northeastern California, southern Oregon, and northwestern Nevada, lies at an intersection between two tectonic provinces; the Basin and Range province and the Cascade volcanic arc. Both of these provinces have substantial geothermal resource base and resource potential. Geothermal systems with evidence of magmatic heat, associated with Cascade arc magmatism, typify the western side of the region. Systems on the eastern side of the region appear to be fault controlled with heat derived from high crustal heat flow, both of which are typical of the Basin and Range. As it has the potential to host Cascade arc-type geothermal resources, Basin and Range-type geothermal resources, and/or resources with characteristics of both provinces, and because there is relatively little current development, the Modoc Plateau region represents an intriguing potential for undiscovered geothermal resources. It remains unclear however, what specific set(s) of characteristics are diagnostic of Modoc-type geothermal systems and how or if those characteristics are distinct from Basin and Range-type or Cascade arc-type geothermal systems. In order to evaluate the potential for undiscovered geothermal resources in the Modoc area, we integrate a wide variety of existing data in order to evaluate geothermal resource potential and exploration risk utilizing ‘play-fairway’ analysis. We consider that the requisite parameters for hydrothermal circulation are: 1) heat that is sufficient to drive circulation, and 2) permeability that is sufficient to allow for fluid circulation in the subsurface. We synthesize data that indicate the extent and distribution of these parameters throughout the Modoc region. ‘Fuzzy logic’ is used to incorporate expert opinion into the utility of each dataset as an indicator of either heat or permeability, and thus geothermal favorability. The results identify several geothermal prospects, areas that are highly favorable for the occurrence of both heat and permeability. These are also areas where there is sufficient data coverage, quality, and consistency that the exploration risk is relatively low. These unknown, undeveloped, and under-developed prospects are well-suited for continued exploration efforts. The results also indicate to what degree the two ‘play-types,’ i.e. Cascade arc-type or Basin and Range-type, apply to each of the geothermal prospects, a useful guide in exploration efforts.

We present a synthetic multi‐scale, multi‐physics dataset constructed from the Kimberlina 1.2 CO2 reservoir model based on a potential CO2 storage site in the Southern San Joaquin Basin of California. Among 300 models, one selected reservoir‐simulation scenario produces hydrologic‐state models at the onset and after 20 years of CO2 injection. Subsequently, these models were transformed into geophysical properties, including P‐ and S‐wave seismic velocities, saturated density where the saturating fluid can be a combination of brine and supercritical CO2, and electrical resistivity using established empirical petrophysical relationships. From these 3D distributions of geophysical properties, we have generated synthetic time‐lapse seismic, gravity and electromagnetic responses with acquisition geometries that mimic realistic monitoring surveys and are achievable in actual field situations. We have also created a series of synthetic well logs of CO2 saturation, acoustic velocity, density and induction resistivity in the injection well and three monitoring wells. These were constructed by combining the low‐frequency trend of the geophysical models with the high‐frequency variations of actual well logs collected at the potential storage site. In addition, to better calibrate our datasets, measurements of permeability and pore connectivity have been made on cores of Vedder Sandstone, which forms the primary reservoir unit. These measurements provide the range of scales in the otherwise synthetic dataset to be as close to a real‐world situation as possible. This dataset consisting of the reservoir models, geophysical models, simulated time‐lapse geophysical responses and well logs forms a multi‐scale, multi‐physics testbed for designing and testing geophysical CO2 monitoring systems as well as for imaging and characterization algorithms. The suite of numerical models and data have been made publicly available for downloading on the National Energy Technology Laboratory's (NETL) Energy Data Exchange (EDX) website. [ABSTRACT FROM AUTHOR]

Electrical resistivity may contribute to progress in understanding geothermal systems by imaging the geometry, bounds and controlling structures in existing production, and thereby perhaps suggesting new areas for field expansion. To these ends, a dense grid of magnetotelluric (MT) stations plus a single line of contiguous bipole array profiling has been acquired over the east flank of the Coso geothermal system. Acquiring good quality MT data in producing geothermal systems is a challenge due to production related electromagnetic (EM) noise and, in the case of Coso, due to proximity of a regional DC intertie power transmission line. To achieve good results, a remote reference completely outside the influence of the dominant source of EM noise must be established. Experimental results so far indicate that emplacing a reference site in Amargosa Valley, NV, 65 miles from the DC intertie, is still insufficient for noise cancellation much of the time. Even though the DC line EM fields are planar at this distance, they remain coherent with the nonplanar fields in the Coso area hence remote referencing produces incorrect responses. We have successfully unwrapped and applied MT times series from the permanent observatory at Parkfield, CA, and these appear adequate to suppress the interference of the cultural EM noise. The efficacy of this observatory is confirmed by comparison to stations taken using an ultra-distant reference site east of Socorro, NM. Operation of the latter reference was successful by using fast ftp internet communication between Coso Junction and the New Mexico Institute of Mining and Technology, using the University of Utah site as intermediary, and allowed referencing within a few hours of data downloading at Coso. A grid of 102 MT stations was acquired over the Coso geothermal area in 2003 and an additional 23 stations were acquired to augment coverage in the southern flank of the first survey area in 2005. These data have been inverted to a fully three-dimensional conductivity model. Initial analysis of the Coso MT data was carried out using 2D MT imaging. An initial 3D conductivity model was constructed from a series of 2D resistivity images obtained using the inline electric field measurements (Zyx impedance elements) along several measurement transects. This model was then refined through a 3D inversion process. This model shows the controlling geological structures possibly influencing well production at Coso and correlations with mapped surface features such as faults and regional geoelectric strike. The 3D model also illustrates the refinement in positioning of conductivity contacts when compared to isolated 2D inversion transects. The conductivity model has also been correlated with microearthquake locations, well fluid production intervals and most importantly with an acoustic and shear velocity model derived by Wu and Lees (1999). This later correlation shows the near-vertical high conductivity structure on the eastern flank of the producing field is also a zone of increased acoustic velocity and increased Vp/Vs ratio bounded by mapped fault traces. South of the Devil's Kitchen is an area of high geothermal well density, where highly conductive near surface material is interpreted as a clay cap alteration zone manifested from the subsurface geothermal fluids and related geochemistry. Beneath the clay cap, however, the conductivity is nondescript, whereas the Vp/Vs ratio is enhanced over the production intervals. It is recommended that more MT data sites be acquired to the southwest of the Devil's Kitchen area to better refine the conductivity model in that area.

Multiphysics inversion exploits different types of geophysical data that often complement each other and aims to improve overall imaging resolution and reduce uncertainties in geophysical interpretation. Despite the advantages, traditional multiphysics inversion is challenging because it requires a large amount of computational time and intensive human interactions for preprocessing data and finding trade‐off parameters. These issues make it nearly impossible for traditional multiphysics inversion to be applied as a real‐time monitoring tool for geological carbon storage. In this paper, we present a deep learning (DL) multiphysics network for imaging CO2 saturation in real time. The multiphysics network consists of three encoders for analysing seismic, electromagnetic and gravity data and shares one decoder for combining imaging capabilities of the different geophysical data for better predicting CO2 saturation. The network is trained on pairs of CO2 label models and multiphysics data so that it can directly image CO2 saturation. We use the bootstrap aggregating method to enhance the imaging accuracy and estimate uncertainties associated with CO2 saturation images. Using realistic CO2 label models and multiphysics data derived from the Kimberlina CO2 storage model, we evaluate the performance of the deep learning multiphysics network and compare its imaging results to those from the deep learning single‐physics networks. Our modelling experiments show that the deep learning multiphysics network for seismic, electromagnetic, and gravity data not only improves the imaging accuracy but also reduces uncertainties associated with CO2 saturation images. Our results also suggest that the deep learning multiphysics network for the non‐seismic data (i.e., electromagnetic and gravity) can be used as an effective low‐cost monitoring tool in between regular seismic monitoring. [ABSTRACT FROM AUTHOR]

Because of their wide application within the petroleum industry it is natural to consider geophysical techniques for monitoring of CO2 movement within hydrocarbon reservoirs, whether the CO2 is introduced for enhanced oil/gas recovery or for geologic sequestration. Among the available approaches to monitoring, seismic methods are by far the most highly developed and applied. Due to cost considerations, less expensive techniques have recently been considered. In this article, the relative merits of gravity and electromagnetic (EM) methods as monitoring tools for geological CO2 sequestration are examined for two synthetic modeling scenarios. The first scenario represents combined CO2 enhanced oil recovery (EOR) and sequestration in a producing oil field, the Schrader Bluff field on the north slope of Alaska, USA. The second scenario is a simplified model of a brine formation at a depth of 1,900 m.

A prototype active electromagnetic system has been developed for detecting and characterizing UXO. The system employs two orthogonal vertical loop transmitters and a pair of horizontal loop transmitters spaced apart vertically by 0.7 m. Eight vertical field detectors are deployed in the plane of each of the horizontal loops and are arranged to measure offset vertical gradients of the fields. The location and orientation of the three principal polarizabilities of a target can be recovered from a single position of the transmitter-receiver system. Further characterization of the target is obtained from the broadband response. The system employs a bipolar half sine pulse train current waveform and the detectors are dB/dt induction coils designed to minimize the transient response of the primary field pulse. The target transient is recovered in a 40 mu-sec to 1.0 msec window. The ground response imposes an early time limit on the time window and system/ambient noise limits the late time response. Nevertheless for practical transmitter moments and optimum receivers the size and the ratio of conductivity to permeability can be accurately recovered. The prototype system has successfully recovered the depths and polarizabilities of ellipsoidal test targets.

An optimally designed active electromagnetic system (AEM), Berkeley UXO Discriminator, BUD, has been developed for detection and characterization of UXO in the 20 mm to 150 mm size range. The system incorporates three orthogonal transmitters, and eight pairs of differenced receivers. The transmitter-receiver assembly together with the acquisition box, as well as the battery power and GPS receiver, is mounted on a small cart to assure system mobility. BUD not only detects the object itself but also quantitatively determines its size, shape, orientation, and metal content (ferrous or non-ferrous, mixed metals). Moreover, the principal polarizabilities and size of a metallic target can be determined from a single position of the BUD platform. The search for UXO is a two-step process. The object must first be detected and its location determined then the parameters of the object must be defined. A satisfactory classification scheme is one that determines the principal dipole polarizabilities of a target. While UXO objects have a single major polarizability (principal moment) coincident with the long axis of the object and two equal transverse polarizabilities, the scrap metal has all three principal moments entirely different. This description of the inherent polarizabilities of a target is a major advance in discriminating UXO from irregular scrap metal. Our results clearly show that BUD can resolve the intrinsic polarizabilities of a target and that there are very clear distinctions between symmetric intact UXO and irregular scrap metal. Target properties are determined by an inversion algorithm, which at any given time inverts the response to yield the location (x, y, z) of the target, its attitude and its principal polarizabilities (yielding an apparent aspect ratio). Signal-to-noise estimates (or measurements) are interpreted in this inversion to yield error estimates on the location, attitude and polarizabilities. This inversion at a succession of times provides the polarizabilities as a function of time, which can in turn yield the size, true aspect ratio and estimates of the conductivity and permeability of the target. The accuracy of these property estimates depends on the time window over which the polarizability measurements, and their accuracies, are known. Initial tests at a local site over a variety of test objects and inert UXOs showed excellent detection and characterization results within the predicted size-depth range. This research was funded by the U.S. Department of Defense under ESTCP Project # UX-0437.

Seismic imaging has been used very successfully to map the location of injected CO2 at the Sleipner and Weyburn Projects (Chadwick et al., 2005; White, 2005). Theoretical calculations by Myer et al. (2003) suggested that the detection limits for cone-shaped accumulations would be in the range of 10,000 20,000 tonnes of CO2. However, based on actual experience, lower limits for detection of CO2 accumulations at the reservoir depth have been estimated to be in the range of several thousand tonnes of CO2. Recently, a VSP obtained at the Frio Brine Pilot demonstrated the ability to detect as little as 1600 tonnes of CO2.The ability to detect small quantities of CO2 that have unintentionally escaped from the storage formation is essential for providing early warning that the project is not performing as expected. The purpose of this study is to assess, from a theoretical and practical perspective, the minimum amount of CO2 that could be detected using surface seismic imaging as it migrated towards the surface. To this end, calculations have been made to assess the seismic response of accumulations of 1,000 tonnes of CO2 at depths of 1300 m, 1000 m, 800 m, and 500 m below the land surface. Sensitivity studies have been conducted to assess the influence of the accumulation thickness and saturation. Sensitivity studies will also be done to determine if even smaller accumulations could be detected at a depth of 500 m or less. In addition, the influence of multi-layer accumulations will also be assessed.

A prototype active electromagnetic system has been developed for detecting and characterizing UXO. The system employs two orthogonal vertical loop transmitters and a pair of horizontal loop transmitters spaced apart vertically by 0.7 m. Eight vertical field detectors are deployed in the plane of each of the horizontal loops and are arranged to measure offset vertical gradients of the fields. The location and orientation of the three principal polarizabilities of a target can be recovered from a single position of the transmitter-receiver system. Further characterization of the target is obtained from the broadband response. The system employs a bipolar half sine pulse train current waveform and the detectors are dB/dt induction coils designed to minimize the transient response of the primary field pulse. The target transient is recovered in a 40 mu sec to 1.0 msec window. The ground response imposes an early time limit on the time window and system/ambient noise limits the late time response. Nevertheless for practical transmitter moments and optimum receivers the size and the ratio of conductivity to permeability can be accurately recovered. The prototype system has successfully recovered the depths and polarizabilities of ellipsoidal test targets.

Knowledge of the subsurface electrical resistivity/conductivity can contribute to a better understanding of complex hydrothermal systems, typified by Coso geothermal field, through mapping the geometry (bounds and controlling structures) over existing production. Three-dimensional magnetotelluric (MT) inversion is now an emerging technology for characterizing the resistivity structures of complex geothermal systems. The method appears to hold great promise, but histories exploiting truly 3D inversion that demonstrate the advantages that can be gained by acquiring and analyzing MT data in three dimensions are still few in number. This project will address said issue, by applying 3D MT forward modeling and inversion to a MT data set acquired over the Coso geothermal field. The goal of the project is to provide the capability to image large geothermal reservoirs in a single self-consistent model. Initial analysis of the Coso MT data has been carried out using 2D MT imaging technology to construct an initial 3D resistivity model from a series of 2D resistivity images obtained using the inline electric field measurements (Zxy impedance elements) along different measurement transects. This model will be subsequently refined through a 3D inversion process.The initial 3D resisitivity model clearly shows the controlling geological structures possibly influencing well production at Coso. The field data however, also show clear three dimensionality below 1 Hz, demonstrating the limitations of 2D resistivity imaging. The 3D MT predicted data arising from this starting model show good correspondence in dominant components of the impedance tensor (Zxy and Zyx) above 1Hz. Below 1 Hz there is significant differences between the field data and the 2D model data.