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Record W2024653442 · doi:10.2118/133587-ms

Research Progress of Modelling on Cold Heavy Oil Production with Sand

2010· article· en· W2024653442 on OpenAlex
Yi Pan, Zhangxin Chen, Jian Sun, Xia Bao, Lizhi Xiao, Ruihe Wang

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSPE Western Regional Meeting · 2010
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEnvironmental scienceProduction (economics)Petroleum engineeringOil productionOil fieldGeology

Abstract

fetched live from OpenAlex

Abstract Heavy oil has been playing a critical role in today's world energy supply. The total amount of heavy oil in place is five to ten times greater than that of the current proven conventional crude. One of the recovery methods, which produces both oil and unconsolidated sands, is known as Cold Heavy Oil Production with Sand (CHOPS). The advantages of CHOPS lie in its commercial success as an inexpensive start up application for heavy oil reservoirs as well as its considerable recovery rates. The general reservoir characteristics associated with successful applications of CHOPS have been established, particularly highlighted in thin reservoirs with non-active edge and bottom water. Heavy oil researchers have accumulated local knowledge for the CHOPS fields; particularly, research groups in Alberta have taken integrated approaches to the questions posed by the field success of cold production. CHOPS gives high early production rates and becomes very efficient in the thin reservoirs where some thermal methods have been economically unsuccessful. Aggressive sand production was encountered in California prior to the First World War. Two key mechanisms lead to the success of cold production in laboratory and field studies: foamy oil flow and wormhole network growth. A variety of numerical models are presented and compared in this paper. Such models can be mainly divided into two broad categories: preliminary model and comprehensive model. With a large number of variables still in limited recognition for the complex mechanisms, several models lack capability in fully simulating CHOPS processes, while progress was achieved in modeling the reservoir heterogeneity with the integration of seismic attributes at specific fields. A detailed discussion of the strengths and weaknesses of cold production models is proposed. The paper ends with the future work of modeling proposed on cold production.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.032
GPT teacher head0.278
Teacher spread0.245 · 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