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Record W2084570424 · doi:10.2118/142004-pa

Experimental Study of Stability and Integrity of Cement in Wellbores Used for CO2 Storage

2010· article· en· W2084570424 on OpenAlexafffund
Jose C. Tarco, Karoosh Asghari

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2010
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Regina
FundersNatural Resources CanadaPetroleum Technology Research CentreUniversidad Central del Ecuador
KeywordsCementCasingMaterials sciencePermeability (electromagnetism)Composite materialCompressive strengthChemistryPetroleum engineeringGeology

Abstract

fetched live from OpenAlex

Summary This paper examines the results obtained from several sets of experimental work conducted on cement deterioration in environments similar to those found in CO2 injection and storage projects. In order to investigate various changes in macroscopic and microscopic properties and behaviour of cement in presence of sulphate ions and CO2, several sets of experiments were conducted. The study consisted of preparing and investigating the behaviour of several hundred samples of cement in presence of various concentrations of sulphate, from 0.1 wt% up to 6%, as well as CO2 at 15160 kPa and 55ºC. The effect of sulphate ions on cement was studied at 30ºC, 55ºC and 75º C. The two common classes of cement of Type 10 and Class G were tested during this study. A total of 300 identical cubic and 400 identical cylindrical samples were tested. The change in permeability, compressive strength, and shear and hydraulic bonding strength for these samples were monitored after 2, 4, 6, 8, 10 and 12 months. Laboratory results showed that sulphate ions and CO2 improve the performance of cement during the first few months. However, the effect is reversed under prolonged experiments. The highest reduction in performance was observed for hydraulic shear bonding, which indicates that the highest risk of CO2 leakage is through pathways between the cement and casing.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.010
GPT teacher head0.221
Teacher spread0.210 · 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 designBench or experimental
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

Citations24
Published2010
Admission routes2
Has abstractyes

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