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Record W2172719149 · doi:10.2118/137001-ms

Impact of Oil Viscosity Gradient Presence on SAGD

2010· article· en· W2172719149 on OpenAlexafffund
J. X. Chen, Y. Ito

Bibliographic record

VenueCanadian Unconventional Resources and International Petroleum Conference · 2010
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsAlberta Innovates
FundersAlberta Innovates - Technology Futures
KeywordsPetroleum engineeringViscositySteam injectionOil viscosityOil fieldEnvironmental scienceTemperature gradientGeologyMaterials scienceMeteorologyPhysics

Abstract

fetched live from OpenAlex

Abstract The presence of an oil viscosity gradient appears to be a real phenomenon in the field and its influence on the SAGD process has been studied by various researchers. However, there is potential to refine previous results to exploit the impact of viscosity gradients on SAGD oil recovery process performance. This paper covers our simulation study of the impact of an oil viscosity gradient presence on SAGD operation. Various SAGD operating scenarios in the reservoir with the presence of a vertical oil viscosity gradient were investigated and simulated. The conclusions are summarized as follows: The presence of a vertical oil viscosity gradient in the reservoir had a mild effect on the SAGD oil recovery rate, while the effect on the oil recovery factor was insignificant. J-Well SAGD, in which a J-well replaces the horizontal producer in a standard SAGD well configuration, did not perform as well as SAGD with a standard well configuration (two parallel horizontal wells). These two conclusions are not in agreement with previous findings by other research groups. Observations from some SAGD field tests indicate the steam chamber could fail to reach the top of reservoirs. For those cases, gas injection could potentially help to recover the oil from the layer above the steam chamber. If that is true, the presence of an oil viscosity gradient might have some impact on SAGD performance with gas injection. Since local steam trap control is required for J-Well SAGD operation in order to optimize performance, the challenge and feasibility of local steam trap control are also discussed in this paper. Some of the SAGD field evidence will be provided to support our simulation results.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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 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

Citations3
Published2010
Admission routes2
Has abstractyes

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