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Record W2158569257

Seismic Monitoring of Cold Heavy Oil Production

2007· article· en· W2158569257 on OpenAlexaffabout
Tingge Wang, Larry Lines, Joan Embleton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOil productionPorosityPetroleum engineeringEnvironmental scienceOil sandsGeologyPetroleumGeotechnical engineeringMaterials science
DOInot available

Abstract

fetched live from OpenAlex

Summary Cold production of heavy oil sands accounts for significant oil production in Canada. In order to enhance heavy oil cold production, seismic monitoring of the reservoir can be used to determine production footprints and allow for optimum infill drilling. The cold heavy oil production with sand (CHOPS) was pioneered in Canada, by mid 1990s, CHOPS became the primary heavy oil production method in Canada. To date CHOPS has achieved wide utilization in Canada and Venezuela, and there is successful use of the technology in China as well. CHOPS is a non-thermal process in which heavy oil and sand are simultaneously extracted and produced by using powerful progressive cavity pumps. The simultaneous extraction of oil and sand generates high porosity channels termed wormholes. It is believed that the wormholes play an important role in heavy oil production due to their permeability effects in the heavy oil reservoirs. The development of wormholes causes the reservoir pressure to fall below the bubble point, and the dissolved-gas comes out of solution to form foamy oil. The formation of foamy oil then causes a partially gas saturated reservoir. In CHOPS, development of wormholes increases porosity in the reservoir. This could change the stress and rigidity of sand matrix in the reservoir, and the changes could result in velocity variations of the reservoir rocks. Based on laboratory experiments, Han et al. (1986) concluded that the measured Vp and Vs decreased dramatically with an increased porosity, and generally the effects of porosity on Vs is larger than on Vp. Formation of foamy oil results in a higher gas saturation in the reservoir, and this could also affect seismic velocity. Toksoz et al. (1976) demonstrated that in partial gas saturation rocks, a small amount of gas can lower the Vp significantly, Domenico (1976, 1977) further concluded that a small amount of gas in sediments diminishes Vp significantly, whereas Vs is insensitive to the presence of gas. Based on the laboratory experiments, Lee (2004) found that the amount of gas and the mode of gas saturation in the pore space dramatically affects the Vp, but not the Vs.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.231
Teacher spread0.213 · 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 designObservational
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
Published2007
Admission routes2
Has abstractyes

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