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Record W2230543304 · doi:10.1088/1742-2132/12/4/702

A simulation research on evaluation of development in shale oil reservoirs by near-miscible CO<sub>2</sub> flooding

2015· article· en· W2230543304 on OpenAlex

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

VenueJournal of Geophysics and Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPetroleum engineeringOil shaleShale oilFlooding (psychology)Shell in situ conversion processTight oilEconomic shortageEnvironmental scienceUnconventional oilPetroleumGeologyShale gasFossil fuelWaste managementEngineering

Abstract

fetched live from OpenAlex

Shale oil is a key resource that could mitigate the impending energy shortage in the future. Despite its abundance in China, studies on shale oil are still at the preliminary stage. Shale oil development through CO2 flooding has been successfully implemented in the United States. Therefore, the mechanics of CO2 flooding in shale oil reservoirs should be investigated. This study applies a simulation method to evaluate the development efficiency of CO2 flooding in shale oil reservoirs. Near-miscible CO2 flooding can effectively develop shale oil. After 20 years, recovery could improve by up to 9.56% as a result of depletion development under near-miscible CO2 flooding with 0.5% pore volume gas injection. Horizontal well injection is better than vertical well injection in terms of sweep efficiency and recovery. Cyclic gas injection is superior to continuous gas injection because the former reduces gas channelling. Thus, the use of horizontal wells with near-miscible cyclic gas injections has the potential to effectively develop shale oil 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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.069
GPT teacher head0.313
Teacher spread0.244 · 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