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Record W2020592327 · doi:10.1016/j.egypro.2013.06.343

CO2 Storage Contingencies Initiative: Detection, Intervention and Remediation of Unexpected CO2 Migration

2013· article· en· W2020592327 on OpenAlexfundno aff
Scott W. Imbus, Kevin J. Dodds, Claus J. Otto, Robert Trautz, Charles A. Christopher, Anshul Agarwal, Sally M. Benson

Bibliographic record

VenueEnergy Procedia · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsnot available
FundersIndustry Canada
KeywordsIntervention (counseling)EngineeringScale (ratio)Environmental planningRisk analysis (engineering)Operations managementEnvironmental resource managementEnvironmental scienceBusinessGeographyCartography

Abstract

fetched live from OpenAlex

High profile refinery, pipeline and well incidents over the past several years may have impacted stakeholder perception of the oil and gas industry's ability to prevent and control accidents. This perception may be translated to current and future CO2 storage projects, as this relatively new application has a limited track record at scale. To better understand unexpected fluid migration and responses, the CO2 Capture Project Phase 3 (CCP3) has developed the “CO2 Storage Contingencies” project. The project was initiated by a CCP3-sponsored workshop that brought together industry, national laboratory and academic experts in wells, reservoir engineering and geosciences. The goal was to systematically assess CO2 and displaced fluid migration scenarios with current versus needed capabilities for detection, intervention and remediation of damages. Three focus areas were addressed in detail: wells, conformance and seals/fractures. It was concluded that groundwater and vadose zone remediation strategies would be deferred owing to decades of related experience and that a focus on intervention might obviate their need. The underlying assumption of the workshop was that even if CO2 storage projects employ state-of-the-art site characterization, risk assessment, and monitoring systems, unexpected migration may nevertheless occur, particularly during the early stages of gaining experience with large scale deployment. Specific mechanisms for unanticipated CO2 and brine migration were identified with both established and novel mitigation approaches for remedying them proposed. The group concurred on a general approach to qualifying scenarios with potential mitigations. A forward plan was outlined to document relevant industry experience with a roadmap of needed research and development (R&D), including modelling, simulation, bench-scale experiments and field trial design through deployment. The initial phases of the study are ongoing with concurrent concept development of the latter phases entailing identification, assessment and possible deployment of a field trial of detection and intervention approaches.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.215
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2013
Admission routes1
Has abstractyes

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