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Record W2085887394 · doi:10.1080/10916466.2010.511388

The Lessons Learned From Miscible Gas Flooding in Naturally Fractured Reservoirs: Integrated Studies, and Pilot and Field Cases

2012· article· en· W2085887394 on OpenAlex
Benyamin Yadali Jamaloei, Riyaz Kharrat

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePetroleum Science and Technology · 2012
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPetroleum engineeringScale (ratio)Enhanced oil recoveryOil fieldEnvironmental scienceFossil fuelProcess engineeringWaste managementEngineering

Abstract

fetched live from OpenAlex

Abstract Suitable methods have to be employed for secondary and tertiary oil recovery from the naturally fractured reservoirs (NFRs). The miscible gas injection has shown some promising results for enhancing oil recovery from NFRs. However, proper design of the field-scale miscible gas injection projects in NFRs is still a major challenge. The authors evaluate the technical issues of the miscible gas injection in NFRs. The classification of NFRs and their production characteristics, the mechanisms of oil production in NFRs, and significant findings of integrated studies, pilot and field trials, and commercial field projects of the miscible gas injection in NFRs are reviewed. Finally, important issues are identified, which need detailed investigations for the design and performance assessment of the field projects. It is hoped that this paper will serve as a helpful reference for the engineers interested in miscible gas injection process in NFRs. Keywords: fieldpilotlarge-scale project designmiscible gas injectionnaturally fractured reservoirs

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.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.038
GPT teacher head0.296
Teacher spread0.258 · 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