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Record W2967380027 · doi:10.2172/1557141

CO<sub>2</sub> Leakage During EOR Operations – Analog Studies to Geologic Storage of CO<sub>2</sub>

2019· report· en· W2967380027 on OpenAlexfundno aff
Derek Vikara, Anna Wendt, Michael Marquis, Timothy Grant, Rana Rassipour, Jeffrey Eppink, Tom L. Heidrick, Ramón W. Alvarado, Allison Guinan, Chung Yan Shih, ShangMin Lin

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsnot available
FundersNational Energy Technology LaboratoryPetroleum Technology Research CentreU.S. Department of Energy
KeywordsMaterials science

Abstract

fetched live from OpenAlex

This study focuses on CO2 EOR operations and CO2 geologic storage in saline-bearing formations; both individually and in relation to each other. This study is the third of three that evaluate analog industries of CO2 storage (first focuses on underground natural gas storage and second on deep well waste disposal using U.S. EPA UIC Class I wells). Several pieces of information can be found throughout the study including overview of CO2 EOR history and side-by-side comparisons of major synergistic features between CO2 EOR and CO2 storage in saline-bearing formations. Best practices and lessons learned from CO2 EOR operations can provide perspective from which future CO2 storage R&D pursuits and field projects can benefit. A summary document providing a high-level overview and key takeaways of this study is available on NETL’s website under the Collection Name: NETL’s Analog Studies to Geologic Storage of CO2.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.040
GPT teacher head0.316
Teacher spread0.276 · 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 designSimulation or modeling
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

Citations2
Published2019
Admission routes1
Has abstractyes

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