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Record W2747219315 · doi:10.1016/j.egypro.2017.03.1620

Pressure Management of CO2 Storage by Closed-loop Surface Dissolution

2017· article· en· W2747219315 on OpenAlexaff
Yun Wu, Steven L. Bryant, Larry W. Lake

Bibliographic record

VenueEnergy Procedia · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of Calgary
FundersUniversity of Texas at Austin
KeywordsDissolutionCabin pressurizationClosed loopPetroleum engineeringAquiferEnvironmental scienceBrineBoundary value problemLead (geology)MechanicsMaterials scienceControl theory (sociology)Computer scienceGeologyEngineeringGeotechnical engineeringThermodynamicsMathematicsGroundwaterChemical engineeringPhysicsControl engineeringComposite material

Abstract

fetched live from OpenAlex

The closed-loop version of surface dissolution strategy is one of the safest techniques for CO 2 storage as it greatly reduces the risks associated with buoyant CO 2 leakage and aquifer pressurization, and eliminates the problem of brine disposal. However, large injection rates are crucial for the feasibility of closed-loop surface dissolution, and these rates could be constrained by the operation strategies and the buildup of average reservoir pressure. In this work, we compare two operating strategies to attain the optimal safe storage rates and find that the optimal operation is the multiple-rates injection. This provides the maximum injection rates and smallest well counts needed to satisfy a given storage target. The impact of elevated average reservoir pressure under infinite-acting boundary condition is quantified and evaluated for injection operation up to 30 years.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.241
Teacher spread0.232 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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