The Stochastic Engine Initiative: Improving Prediction of Behavior in Geologic Environments We Cannot Directly Observe

Aines, Roger D.; Nitao, John J.; Newmark, R. L.; Carle, Steven F.; Ramirez, Abelardo; Harris, Dominique; Johnson, James W.; Johnson, _______; Ermak, D; Sugiyama, G.; Hanley, William G.; Sengupta, Sankar; Daily, William A.; Glaser, Ronald E.; Dyer, Kathleen M.; Fogg, Graham E.; Zhang, Y; Yu, Zehao; Levine, Robert Y.

doi:10.2172/15002143

Cited by 11 publications

(18 citation statements)

References 8 publications

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“…Here we briefly outline the general concepts of the Bayesian MCMC methodology used in our work. For further details on its theory and application to geophysical inverse problems, see, for example, Mosegaard and Tarantola [1995], Bosch [1999], Aines et al [2002], and Ramirez et al [2005]. We begin by considering n sets of measured data { d 1 , …, d n } that each informs us in some way about the subsurface environment, and the vector m that contains the model parameters of interest, in our case the spatial configuration of K. Regarding m as a multivariate probability distribution, Bayes' Theorem can be used to update our initial state of knowledge about these parameters into a more refined state of knowledge given the available data.…”

Section: Inversion Methodologymentioning

confidence: 99%

“…In recent years, a number of papers have appeared in the geophysical literature that treat the complex data integration and inversion problem for spatially distributed subsurface properties in a fully stochastic manner using Bayes' Theorem [e.g., Mosegaard and Tarantola , 1995; Bosch , 1999; Aines et al , 2002; Eidsvik et al , 2002; Ramirez et al , 2005]. Once thought computationally impractical, the results in these papers have demonstrated that with modern computational resources and state‐of‐the‐art forward simulation and sampling algorithms, such stochastic data integration is feasible for real‐world problems.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities

Irving

Singha

2010

Water Resources Research

116

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[1] Quantifying the spatial configuration of hydraulic conductivity (K) in heterogeneous geological environments is essential for accurate predictions of contaminant transport, but is difficult because of the inherent limitations in resolution and coverage associated with traditional hydrological measurements. To address this issue, we consider crosshole and surface-based electrical resistivity geophysical measurements, collected in time during a saline tracer experiment. We use a Bayesian Markov-chain-Monte-Carlo (McMC) methodology to jointly invert the dynamic resistivity data, together with borehole tracer concentration data, to generate multiple posterior realizations of K that are consistent with all available information. We do this within a coupled inversion framework, whereby the geophysical and hydrological forward models are linked through an uncertain relationship between electrical resistivity and concentration. To minimize computational expense, a facies-based subsurface parameterization is developed. The Bayesian-McMC methodology allows us to explore the potential benefits of including the geophysical data into the inverse problem by examining their effect on our ability to identify fast flowpaths in the subsurface, and their impact on hydrological prediction uncertainty. Using a complex, geostatistically generated, two-dimensional numerical example representative of a fluvial environment, we demonstrate that flow model calibration is improved and prediction error is decreased when the electrical resistivity data are included. The worth of the geophysical data is found to be greatest for long spatial correlation lengths of subsurface heterogeneity with respect to wellbore separation, where flow and transport are largely controlled by highly connected flowpaths.Citation: Irving, J., and K. Singha (2010), Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities, Water Resour. Res., 46, W11514,

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Section: Inversion Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities

Irving

Singha

2010

Water Resources Research

116

View full text Add to dashboard Cite

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“…A detailed description of the application of the MCMC approach to plume reconstruction is found in Ramirez et al [1]. Importantly, the basic approach was developed in a prior LDRD-SI proposal as described in Aines et al [2] This approach is useful for a variety of subsurface problems such as geophysical inversion, data fusion, and reservoir fluid flow monitoring (water floods, steam injection, CO 2 floods). A key advantage of the approach is that it explicitly treats the nonuniqueness inherent in geophysical inversion.…”

Section: Base Methodologymentioning

confidence: 99%

Integration & Co-development of a Geophysical CO2 Monitoring Suite

Friedmann¹

2007

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Carbon capture and sequestration (CCS) has emerged as a key technology for dramatic short-term reduction in greenhouse gas emissions in particular from large stationary. A key challenge in this arena is the monitoring and verification (M&V) of CO2 plumes in the deep subsurface. Towards that end, we have developed a tool that can simultaneously invert multiple sub-surface data sets to constrain the location, geometry, and saturation of subsurface CO2 plumes. We have focused on a suite of unconventional geophysical approaches that measure changes in electrical properties (electrical resistance tomography, electromagnetic induction tomography) and bulk crustal deformation (tilmeters). We had also used constraints of the geology as rendered in a shared earth model (ShEM) and of the injection (e.g., total injected CO 2 ).We describe a stochastic inversion method for mapping subsurface regions where CO 2 saturation is changing. The technique combines prior information with measurements of injected CO 2 volume, reservoir deformation and electrical resistivity. Bayesian inference and a Metropolis simulation algorithm form the basis for this approach. The method can a) jointly reconstruct disparate data types such as surface or subsurface tilt, electrical resistivity, and injected CO 2 volume measurements, b) provide quantitative measures of the result uncertainty, c) identify competing models when the available data are insufficient to definitively identify a single optimal model and d) rank the alternative models based on how well they fit available data.We present results from general simulations of a hypothetical case derived from a real site. We also apply the technique to a field in Wyoming, where measurements collected during CO 2 injection for enhanced oil recovery serve to illustrate the method's performance. The stochastic inversions provide estimates of the most probable location, shape, volume of the plume and most likely CO 2 saturation. The results suggest that the method can reconstruct data with poor signal to noise ratio and use hard constraints available from many sites and applications. External interest in the approach and method is high, and already commercial and DOE entities have requested technical work using the newly developed methodology for CO 2 monitoring.-2-

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“…This tool may include multiattribute utility theory to evaluate alternatives for complicated problems with multiple objectives (e.g., Dyer et al, 1998), or might involve a stochastic approach to guide optimal sensor placement within distribution systems (e.g., Murray, 2004;Johannesson et al, 2004). Such methodologies have been successfully applied to problems including the disposition of surplus weapons grade plutonium, the siting of an electricity generation facility (Dyer et al, 1998), improved predictions of contaminant transport through geologic material (Aines et al, 2002), and improved sensor and source analysis for atmospheric dispersion problems (Johannesson et al, 2004). Additional fidelity could include the economic considerations for both (1) UCRL-/SAND2005-2/4 employing current technologies in an EWS role for water distribution systems and (2) developing emerging sensor technologies specifically for water distribution system use.…”

Section: Toward a More Detailed Sensor Down Selection Criteriamentioning

confidence: 99%

Sensor Acquisition for Water Utilities: Addendum

Alai¹,

Glascoe²,

Einfeld³

2005

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The Stochastic Engine Initiative: Improving Prediction of Behavior in Geologic Environments We Cannot Directly Observe

Abstract: T h e S t o c h a s t i c En g i n e I n it i a t i ve : I m p r o v i n g Pr e d ic t i o n of Be h a v i o r in Ge ol o g i c En v i r on m e n t s W e C a n n o t D i r e ct l y Ob s e r v e1 4 0 z (m) -1 0 x ( m ) 0 1 0 1 0 0 -1 0 y ( m )

Cited by 11 publications

References 8 publications

Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities

Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities

Integration & Co-development of a Geophysical CO2 Monitoring Suite

Sensor Acquisition for Water Utilities: Addendum

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