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Record W2374886208

STATUS AND DEVELOPMENT TREND OF HEAVY OIL COLD PRODUCTION TECHNOLOGY OF THE WORLD

2002· article· en· W2374886208 on OpenAlexaboutno aff
Dong Benjing

Bibliographic record

VenueDrilling & Production Technology · 2002
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsOil productionOil fieldProduction (economics)Petroleum engineeringEngineeringChinaEnvironmental scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

This paper reviews the status of heavy oil cold production technology of the world,and discusses its development trend in the future.Price decontrol and global drop in oil price in 1986 drove down the cost effectiveness of thermal recovery and heavy oil production in general.Since that time,some small oil companies in Canada started to keep on probing heavy oil cold production technology in fields and gained unexpected success.Up to the mid 90s,cold production with sand had become a hot technical spot for heavy oil development.Besides small oil companies,many large oil companies also set their foot in this field.Some related resarch organizations and institutions had devoted themselves to the study of the mechanism of cold production with sand,putting forward the theory of wormhole and foamy oil.ln 1996,in order to study and extend the heavy oil cold production technology in China,CNPC put it as a key research project of the Ninth-Fivenational development plan,organizing a series of feasibility study and field tests in Henan oilfield.Aladeiba and Bentiu reservoirs of the Fula oilfield in block 6,Sudan are typical unconsolidated heavy oil formations,easy at producing sand,with low pressure.To efficiently develop such reservoirs, CNODC conducted a series of investigation and field tests last year and believed that the technology of cold production with sand is the prospecting one for developing the Fula oilfield.At present,there still exist some problems in aspects of the recognition for cold production mechanism and of the improvement for production technologies.For the future,RD for cold production technology will focus on four hotspots.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.019
GPT teacher head0.238
Teacher spread0.219 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2002
Admission routes1
Has abstractyes

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