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Record W2036123606 · doi:10.1016/j.egypro.2014.11.327

The relationship between kh and achievable rates of injection, and repercussions for large scale storage

2014· article· en· W2036123606 on OpenAlexaff
Abraham Frei-Pearson, Steven L. Bryant

Bibliographic record

VenueEnergy Procedia · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPetroleum engineeringBrineSupercritical fluidEnvironmental scienceInjection wellSoil scienceGeologyChemistry

Abstract

fetched live from OpenAlex

Geologic CO 2 storage (GCS) can provide meaningful reduction of CO 2 emissions if implemented with large injection rates. The traditional paradigm for GCS entails injection of supercritical CO 2 into a brine-filled formation with an implicit assumption that resident brine can be displaced through boundaries of the formation without adverse effects. In this paradigm, kh , the product of permeability and formation thickness, is the first order control on achievable injection rates, and the gradual buildup of pressure in the formation during the storage operation will reduce those rates. These two factors impose serious constraints on the overall storage rate. In contrast, examination of field-aggregated injection and production volumes during waterflooding operations in oil reservoirs reveals a notable lack of correlation between kh and achieved injection rates. This suggests that if CO 2 storage projects are operated in the same manner as waterflooded oil reservoirs, i.e. with both injection and extraction wells, located and operated to maximize rates, then material rates of storage can be achieved regardless of reservoir kh . When applied to a large set of storage formations, this mode of operation provides an otherwise unattainable overall rate of storage while greatly reducing risks associated with elevated pressure in storage regions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.269
Teacher spread0.252 · 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.

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

Citations2
Published2014
Admission routes1
Has abstractyes

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