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Record W4405992969 · doi:10.1080/10916466.2024.2447885

Characteristics and control methods of high-pressure in-situ combustion by heavy oil reservoir

2025· article· en· W4405992969 on OpenAlexaff
Neng Gao, Haiyan Jiang, Yuanxian Liu, Tianyue Li, Shuai Liu, Xidong Cai, Pingge Jiao

Bibliographic record

VenuePetroleum Science and Technology · 2025
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsPetro-Canada
FundersNational Natural Science Foundation of China
KeywordsPetroleum engineeringIn situCombustionEnvironmental scienceChemistryGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

With the increase of buried depth, the in-situ combustion (ISC) of deep heavy oil reservoirs faces the problems of unclear high-pressure oxidation mechanism and high risk, and the conventional steam injection method cannot guarantee the effect. Taking Lukeqin Oilfield in Xinjiang, China, as an example, the oxidation characteristics of heavy oil under different pressures are analyzed by thermal analysis and combustion tube experiments, and the feasibility of high-pressure ISC is analyzed. The control method of high-pressure ISC in the field is obtained by numerical simulation. The results show that high pressure advances the oxidation stages of crude oil and accelerates the advance rate of the combustion front. The temperature of the combustion front is increased by more than 300 °C, but the combustion stability becomes worse. The activation energy and pressure of each oxidation stage show a power function decreasing relationship. High-pressure low-oxygen ISC reduces the ignition threshold temperature, and the combustion is stable and continuous, which is feasible in the development of deep heavy oil reservoirs. When the oxygen content is 8%, the combustion of high-pressure ISC is stable, the recovery rate is increased by 8%.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.006
GPT teacher head0.274
Teacher spread0.267 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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