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Record W2016505724 · doi:10.2118/110577-ms

Laboratory Experimental Results of Huff ‘n’ Puff CO2 Flooding in a Fractured Core System

2007· article· en· W2016505724 on OpenAlexaff
K. Asghari, Farshid Torabi

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2007
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPetroleum engineeringFracture (geology)Core (optical fiber)Tight oilMaterials scienceDecaneMatrix (chemical analysis)GeologyEnvironmental scienceComposite materialChemistry

Abstract

fetched live from OpenAlex

Abstract In this study, the performance and efficiency of CO2 huff-and-puff process for improving oil recovery and subsequent storage of CO2 in fractured porous media is examined and the results of laboratory tests are presented. The experimental set up consisted of a high-pressure stainless steel cell made specially to hold a cylindrical core with spacing around it to simulate fractures surrounding matrix. The matrix was saturated with normal decane, which was used as oil during these experiments. Over six sets of huff-and-puff experiments, using CO2 as solvent, were conducted for pressures of 250, 500, 750, 1000, 1250, and 1500 psi. Each set of the huff-and-puff experiments were conducted by injecting CO2 in the fracture surrounding the core (injection step). Then, the system was shut-in for a period of 24 hours to allow CO2 to diffuse from fracture into the oil in matrix (soaking period step). At the end of soaking period, the pressure was released and the oil production was measured (production step). The above cycle was repeated until no more oil was produced. The results obtained showed that CO2 huff-and-puff process improves the oil production from fractured media, significantly. These results also indicate that the oil recovery is higher for huff-and-puff experiments conducted at higher pressures.

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 score0.016
Threshold uncertainty score0.658

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 head0.015
GPT teacher head0.266
Teacher spread0.251 · 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
Published2007
Admission routes1
Has abstractyes

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