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Record W2258229514 · doi:10.2172/1126712

Basin-Scale Leakage Risks from Geologic Carbon Sequestration: Impact on Carbon Capture and Storage Energy Market Competitiveness

2014· report· en· W2258229514 on OpenAlexaboutno aff
Catherine A. Peters, Jeffrey P. Fitts, ELIZABETH WILSON, Melisa F. Pollak, Jeffrey M. Bielicki, Vatsal Bhatt

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon sequestrationLeakage (economics)AquiferEnvironmental scienceStructural basinHydrology (agriculture)GeologyPetroleum engineeringGroundwaterCarbon dioxideGeotechnical engineeringGeomorphology

Abstract

fetched live from OpenAlex

This three-year project, performed by Princeton University in partnership with the University of Minnesota and Brookhaven National Laboratory, examined geologic carbon sequestration in regard to CO{sub 2} leakage and potential subsurface liabilities. The research resulted in basin-scale analyses of CO{sub 2} and brine leakage in light of uncertainties in the characteristics of leakage processes, and generated frameworks to monetize the risks of leakage interference with competing subsurface resources. The geographic focus was the Michigan sedimentary basin, for which a 3D topographical model was constructed to represent the hydrostratigraphy. Specifically for Ottawa County, a statistical analysis of the hydraulic properties of underlying sedimentary formations was conducted. For plausible scenarios of injection into the Mt. Simon sandstone, leakage rates were estimated and fluxes into shallow drinking-water aquifers were found to be less than natural analogs of CO{sub 2} fluxes. We developed the Leakage Impact Valuation (LIV) model in which we identified stakeholders and estimated costs associated with leakage events. It was found that costs could be incurred even in the absence of legal action or other subsurface interference because there are substantial costs of finding and fixing the leak and from injection interruption. We developed a model framework called RISCS, which can be used to predict monetized risk of interference with subsurface resources by combining basin-scale leakage predictions with the LIV method. The project has also developed a cost calculator called the Economic and Policy Drivers Module (EPDM), which comprehensively calculates the costs of carbon sequestration and leakage, and can be used to examine major drivers for subsurface leakage liabilities in relation to specific injection scenarios and leakage events. Finally, we examined the competiveness of CCS in the energy market. This analysis, though qualitative, shows that financial incentives, such as a carbon tax, are needed for coal combustion with CCS to gain market share. In another part of the project we studied the role of geochemical reactions in affecting the probability of CO{sub 2} leakage. A basin-scale simulation tool was modified to account for changes in leakage rates due to permeability alterations, based on simplified mathematical rules for the important geochemical reactions between acidified brines and caprock minerals. In studies of reactive flows in fractured caprocks, we examined the potential for permeability increases, and the extent to which existing reactive transport models would or would not be able to predict it. Using caprock specimens from the Eau Claire and Amherstburg, we found that substantial increases in permeability are possible for caprocks that have significant carbonate content, but minimal alteration is expected otherwise. We also found that while the permeability increase may be substantial, it is much less than what would be predicted from hydrodynamic models based on mechanical aperture alone because the roughness that is generated tends to inhibit flow.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.750
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.285
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2014
Admission routes1
Has abstractyes

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