[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.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2009WR008340

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

Stochastic joint inversion of hydrogeophysical data for salt tracer test monitoring and hydraulic conductivity imaging

Geophysical monitoring of the Weyburn CO2 flood: Results during 10 years of injection

Monitoring subsurface flow and transport processes over a wide range of spatiotemporal scales remains one of the greatest challenges in hydrology. Electrical geophysical techniques have been implemented to non-invasively investigate a broad range of subsurface hydrological processes. Recent advances in instrumentation and interpretational tools highlight the emerging opportunities to adopt long term resistivity monitoring (LTRM) to improve understanding of flow and transport processes operating over monthly to decadal timescales that are not adequately captured in short-term monitoring datasets and are temporally aliased in datasets constructed from occasional reoccupation of a study site. The emergence of LTRM as a robust tool in hydrology represents a paradigm shift in geophysical data acquisition and analysis, with resistivity monitoring now evolving into a hydrological decision support technology. We describe the theoretical basis for adopting LTRM for non-invasive monitoring of hydrological state variables over multiple spatial scales and with higher temporal resolution than achieved from periodic reoccupation of a field site. Instrumentation developments facilitating autonomous data acquisition at off the grid field sites are discussed, along with advances in data processing that enhance the hydrological information content inherent in LTRM datasets. Case studies from a diverse range of hydrology subdisciplines highlight the largely untapped potential for LTRM to provide information beyond the reach of established hydrology tools. Future opportunities and challenges relating to the more widespread adoption of LTRM, including addressing inherent uncertainty in resistivity interpretation, upscaling, computational and modelling needs are critically discussed.

Advancing hydrological process understanding from long-term resistivity monitoring systems

Quantitative characterization of subsurface properties is critical for many environmental applications and serves as the basis to simulate and better understand dynamic subsurface processes. Geophysical imaging methods allow to image subsurface property distributions and monitor their spatio-temporal changes in a minimally invasive manner. While it is widely agreed upon that models integrating multiple independent data sources are more reliable, the number of approaches to do so is increasing rapidly and often overwhelming for researchers and, particularly, novices to the field. With this work, we aim to contribute to the development multimethod imaging through (1) an overview of, and didactic introduction to, existing inversion approaches for the integration of multiple geophysical data sets with other measurement types (e.g., hydrological observations), petrophysical models, and process simulations, (2) a state-of-the-art review on the use and potentials of these approaches in various environmental applications, and (3) a discussion on new frontiers and remaining challenges in the field. We hope that this chapter provides an entry point to recent developments in multimethod geophysical imaging, clarifies similarities, differences, and development potentials of existing approaches, and ultimately helps practitioners to choose the optimum one to integrate their data sets.

/pdf/an-overview-of-multimethod-imaging-approaches-in-5dynztdyss.pdf

An overview of multimethod imaging approaches in environmental geophysics

The stochastic engine uses modern computational capabilities to combine simulations with observations. We integrate the general knowledge represented by models with specific knowledge represented by data, using Bayesian inferencing and a highly efficient staged Metropolis-type search algorithm. From this, we obtain a probability distribution characterizing the likely configurations of the system consistent with existing data. The primary use will be optimizing knowledge about the configuration of a system for which sufficient direct observations cannot be made. Programmatic applications include underground systems ranging from environmental contamination to military bunkers, optimization of complex nonlinear systems, and timely decision-making for complex, hostile environments such as battlefields or the detection of secret facilities. We create a stochastic ''base representation'' of system configurations (states) from which the values of measurable parameters can be calculated using forward simulators. Comparison of these predictions to actual measurements drives embedded Bayesian inferencing, updating the distributions of states in the base representation using the Metropolis method. Unlike inversion methods that generate a single bestcase deterministic solution, this method produces all the likely solutions, weighted by their likelihoods. This flexible method is best applied to highly non-linear, multi-dimensional problems. Staging of the Metropolis searches permits us to run the simplest modelmore » systems, such as lithology estimators, at the lower stages. The majority of possible configurations are thus eliminated from further consideration by more complex simulators, such as flow and transport models. Because the method is fully automated, large data sets of a variety of types can be used to refine the system configurations. The most important prerequisites for optimal use of this method are well-characterized forward simulators, realistic base representations, and most importantly an ability to obtain disparate data sets that are directly affected by the system configuration. Our initial earth-sciences application uses models for lithology, flow and transport, geochemistry, and geophysical imaging; the system configuration (base representation) being refined is the rock type at each underground location. In the initial stages of this initiative we demonstrated a two-stage analysis of synthetic Electrical Resistance Tomography (ERT) data and hydraulic flow information (Newmark et al., 2002). We used these results to develop algorithms that improve efficiency of the Metropolis search and provide accurate diagnostic evaluation during the search. Using actual data from a highly contaminated A/M outfall and solvent tank storage areas at the Savannah River Site (SRS), we used the stochastic engine to resolve lithology using ERT data. SRS will use these methods in their design and implementation of steam cleanup of the largest trichloroethylene (TCE) source in the Department of Energy (DOE) complex. We have implemented ''soft conditioning'' algorithms that allow us to use a variety of data types to control the initial representations, and most importantly, to use the final distribution resulting from one stochastic engine analysis as the initial distribution for a subsequent analysis. We have created a web-based interface that will allow collaborators like SRS to enter data and observe results of calculations on Lawrence Livermore National Laboratory (LLNL) supercomputers in an interactive mode. All engine functions operate in three dimensions, and a parallel implementation on Linux cluster machines is in initial testing. The method will be extended to include active process analysis, in which an ongoing data stream is used to continuously update the understanding of the system configuration. Applications to other types of state spaces, such as chemical parameters in a reacting system or atmospheric plume movement, are being evaluated.« less

/pdf/the-stochastic-engine-initiative-improving-prediction-of-1bz0twkovk.pdf

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

Over the last project six months, our project activities have concentrated on three areas: (1) performing a stochastic inversion of pattern 16 seismic data to deduce reservoir permeability, (2) development of the geochemical inversion strategy and implementation of associated software, and (3) completing the software implementation of TProGS and the geostatistical analysis that provides the information needed when using the software to produce realizations of the Midale reservoir. The report partially the following deliverables: D2: Model development: MCMC tool (synthetic fluid chemistry data); deliverable completed. D4: Model development/verification: MCMC tool (TProGS, field seismic/chemistry data) work product; deliverable requirements partially fulfilled. D5: Field-based single-pattern simulations work product; deliverable requirements partially fulfilled. 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 CO{sub 2} storage. Our tool uses the Markov Chain Montemore » 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 CO{sub 2} 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, tested, and used to process seismic data from pattern 16. A previously submitted report titled ''Model verification: synthetic single pattern simulations using seismic reflection data'', Ramirez et al. 2010, partially fulfilled deliverable D3 by summarizing verification activities that evaluate the performance of the seismic software and its ability to recover reservoir model permeabilities using synthetic seismic reflection data. A future progress report will similarly describe summarizing verification activities of the geochemical inversion software, thereby completing deliverable D3. This document includes a chapter that shows and discusses permeability models produced by seismic inversion that used seismic data from pattern 16 in Phase 1A. It partially fulfills deliverable D5: Field-based single-pattern simulations work product. The D5 work product is supposed to summarize the results of applying NUFT/MCMC to refine the reservoir model and geochemical parameters by optimizing observation/prediction agreement for the seismic/geochemical response to CO{sub 2} injection/migration within a single pattern of Phase 1A/1B. A future progress report will show inversion results for the same pattern using geochemical data, thereby completing deliverable D5. This document also contains a chapter that fulfills deliverable D2: Model development: MCMC tool (synthetic fluid chemistry data). The chapter will summarize model development activities required to facilitate application of NUFT/MCMC to optimize agreement between the observed and predicted geochemical response to CO{sub 2} injection/migration. Lastly, this document also contains a chapter that partially fulfills deliverable D4: Model development/verification: MCMC tool (TProGS, field seismic/chemistry data) work product. This work product is supposed to summarize model development activities required for (1) application of TProGS to Weyburn, (2) use of TProGS within the MCMC tool, and (3) application of the MCMC tool to address field seismic and geochemical data. The chapter included here fulfills requirements 1 and 2. Requirement 3 will be addressed in a future progress report.« less

/pdf/progress-report-december-2010-improved-site-characterization-apkhm72lru.pdf

Progress Report, December 2010: Improved Site Characterization And Storage Prediction Through Stochastic Inversion Of Time-Lapse Geophysical And Geochemical Data

During Phase 1 of the Weyburn Project (2000-2004), 4D reflection seismic data were used to map CO{sub 2} migration within the Midale reservoir, while an extensive fluid sampling program documented the geochemical evolution triggered by CO{sub 2}-brine-oil-mineral interactions. The aim of this task (3b.11) is to exploit these existing seismic and geochemical data sets, augmented by CO{sub 2}/H{sub 2}O injection and HC/H{sub 2}O production data toward optimizing the reservoir model and thereby improving site characterization and dependent predictions of long-term CO{sub 2} storage in the Weyburn-Midale reservoir. Our initial project activities have concentrated on developing a stochastic inversion method that will identify reservoir models that optimize agreement between the observed and predicted seismic response. This report describes the technical approach we have followed, the data that supports it, and associated implementation activities. The report fulfills deliverable D1 in the project's statement of work. Future deliverables will describe the development of the stochastic inversion tool that uses geochemical data to optimize the reservoir model.

/pdf/development-of-a-stochastic-inversion-tool-to-optimize-1kxf6kqq2e.pdf

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

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

S Carle

Author Tools

Chat about Author

Papers

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

Progress Report, December 2010: Improved Site Characterization And Storage Prediction Through Stochastic Inversion Of Time-Lapse Geophysical And Geochemical Data

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