Xianjin Yang

Difference Inversion of ERT Data: a Fast Inversion Method for 3-D In Situ Monitoring

A three-dimensional (3-D) Occam’s inversion algorithm for electrical resistivity tomography is modified to allow for inversion on the differences between the background and subsequent data sets. The algorithm is optimized for in situ monitoring applications. The resistivity obtained by the inversion of background data serves as the a priori model in the difference inversion. There are several advantages to this method. First, convergence is fast since the inverse routine needs only to find small perturbations about a good initial guess. Second, systematic errors such as those due to errors in field configuration and discretization errors in the forward modeling algorithm tend to cancel. The result is that we can fit the difference data far more closely than the individual potentials. Better data fits often equate to better resolution with fewer inversion artifacts. The difference inversion technique was applied to monitoring in-situ steam remediation in Portsmouth, Ohio and monitoring of flow in fluid fra...

Chapter 15 Three-dimensional monitoring of vadose zone infiltration using electrical resistivity tomography and cross-borehole ground-penetrating radar

Abstract We discuss results from field experiments conducted at the Sandia-Tech Vadose Zone experimental facility on the New Mexico Tech campus at Socorro, New Mexico, as part of a project to develop a joint hydrological-geophysical method to characterize fluid flow properties of the vadose zone. The site contains dense arrays of tensiometers, access tubes for neutron moisture and ground-penetrating radar probes, and arrays of surface and subsurface electrical resistivity tomography electrodes installed in shallow clays, sands and gravels. We collected electrical resistivity tomography (ERT), cross-borehole ground-penetrating radar (XBGPR) and neutron data before and during a controlled infiltration of water at the site. Using local, empirical relations, we estimated subsurface moisture contents from images generated with the XBGPR and ERT data. The XBGPR images provided an excellent comparison to the neutron-derived moisture contents along a plane through the center of the experimental site. The ERT results were limited in terms of resolution by the coarse electrode spacing and inversion mesh used at the site, but provided a full three-dimensional picture of the wetting front as it progressed.

Advanced geophysical underground coal gasification monitoring

Underground Coal Gasification (UCG) produces less surface impact, atmospheric pollutants and greenhouse gas than traditional surface mining and combustion. Therefore, it may be useful in mitigating global change caused by anthropogenic activities. Careful monitoring of the UCG process is essential in minimizing environmental impact. Here we first summarize monitoring methods that have been used in previous UCG field trials. We then discuss in more detail a number of promising advanced geophysical techniques. These methods – seismic, electromagnetic, and remote sensing techniques – may provide improved and cost-effective ways to image both the subsurface cavity growth and surface subsidence effects. Active and passive seismic data have the promise to monitor the burn front, cavity growth, and observe cavity collapse events. Electrical resistance tomography (ERT) produces near real time tomographic images autonomously, monitors the burn front and images the cavity using low-cost sensors, typically running within boreholes. Interferometric synthetic aperture radar (InSAR) is a remote sensing technique that has the capability to monitor surface subsidence over the wide area of a commercial-scale UCG operation at a low cost. It may be possible to infer cavity geometry from InSAR (or other surface topography) data using geomechanical modeling. The expected signals from these monitoring methods are described along with interpretive modeling for typical UCG cavities. They are illustrated using field results from UCG trials and other relevant subsurface operations.

Model verification: synthetic single pattern simulations using seismic reflection data

During Phase 1 of the Weyburn Project (2000-2004), 4D reflection seismic data were used to map CO2 migration within the Midale reservoir, while an extensive fluid sampling program documented the geochemical evolution triggered by CO2-brine-oilmineral interactions. The aim of this task (3b.11) is to exploit these existing seismic and geochemical data sets, augmented by CO2/H2O injection and HC/H2O production data toward optimizing the reservoir model and thereby improving site characterization and dependent predictions of long-term CO2 storage in the Weyburn-Midale reservoir. Our current project activities have concentrated on completing and testing a stochastic inversion method that will identify reservoir models that optimize agreement between the observed and predicted seismic response. This report describes the results of a validation test that uses synthetic seismic data to identify optimal porosity/permeability distributions within the reservoir. The report partially fulfills deliverable D3: “Model verification: synthetic single pattern simulations” in the project’s statement of work. A future deliverable will describe verification activities related to the geochemical inversion algorithm. This work has been performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. Introduction When completed, our completed stochastic inversion tool will explicitly integrate reactive transport modeling, facies-based geostatistical methods, and a novel stochastic inversion technique to optimize agreement between observed and predicted storage performance. Such optimization will be accomplished through stepwise refinement of: 1) the reservoir model—principally its permeability magnitude and heterogeneity—and 2) geochemical parameters—primarily key mineral volume fractions and kinetic data. We anticipate that these refinements will facilitate significantly improved history matching and forward modeling of CO2 storage. Our tool uses the Markov Chain Monte Carlo (MCMC) methodology. Deliverable D1, previously submitted as a report titled “Development of a Stochastic Inversion Tool To Optimize Agreement Between The Observed And Predicted Seismic Response To CO2 Injection/Migration in the Weyburn-Midale Project” (Ramirez et al., 2009), described the stochastic inversion approach that will identify reservoir models that optimize agreement between the observed and predicted seismic response. The software that implements this approach has been completed and requires that its performance be verified. This document contains deliverable D3, a report that summarizes verification activities that evaluate the performance of the software and its ability to recover reservoir model permeabilities that optimize agreement between measured and predicted seismic reflection data. A future deliverable will describe verification activities that ensure recovery of geochemical parameters (mineral volume fraction, kinetic parameters) that optimize agreement between measured and predicted aqueous chemistry data.