MétaCan
Menu
Back to cohort
Record W2075024565 · doi:10.1080/10916466.2010.490809

A Simulation Study of Carbon Dioxide Sequestration in a Depleted Oil Reservoir

2012· article· en· W2075024565 on OpenAlexaff
Ali Momeni, Masoud Aghajani, Ghasem Zargar

Bibliographic record

VenuePetroleum Science and Technology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCarbon sequestrationCarbon dioxideEnhanced oil recoveryEnvironmental scienceGreenhouse gasNegative carbon dioxide emissionCarbon capture and storage (timeline)Carbon dioxide removalGreenhouse gas removalFossil fuelBio-energy with carbon capture and storageCombustionGlobal warmingPetroleum engineeringCarbon dioxide in Earth's atmosphereWaste managementChemistryClimate changeGeologyEngineering

Abstract

fetched live from OpenAlex

Oil fields offer significant potential for storing carbon dioxide (CO2) and will most likely be the first large-scale geological targets for sequestration because the infrastructure, experience, and permitting procedures already exist. In addition, almost 40 years' experience in enhanced oil recovery (EOR) allows utilization of carbon capture and storage (CCS) and CO2 sequestration techniques in such a way as to improve recovery of petroleum fields and reduce the environmental issue of fossil fuel combustion gas products, particularly carbon dioxide. Carbon dioxide is one of the main greenhouse gases that causes global warming. As a response to global warming, geologic sequestration of CO2 in oil and gas reservoirs is one possibility to reduce the amount of CO2 released to the atmosphere. This simulation study presents a synthetic geologic model that is used to sequestrate carbon dioxide beside an EOR immiscible displacement process.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.017
GPT teacher head0.282
Teacher spread0.264 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

Explore more

Same venuePetroleum Science and TechnologySame topicCO2 Sequestration and Geologic InteractionsFrench-language works237,207