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Record W4249268080 · doi:10.2118/2003-202

Impacts of Gas in SAGD: History Matching of Lab Scale Tests

2003· article· en· W4249268080 on OpenAlexaboutno aff
Junxia Yuan, D.H.S. Law, T.N. Nasr

Bibliographic record

VenueCanadian International Petroleum Conference · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsScale (ratio)Petroleum engineeringMatching (statistics)Computer scienceProcess engineeringEnvironmental scienceEngineeringMathematicsStatisticsGeography

Abstract

fetched live from OpenAlex

Abstract This study addresses the importance of initial GOR in SAGD heavy oil recovery operations. By history matching two laboratory scale experiments, one with dead oil and the other with live oil, we corroborated the theoretical and numerical prediction that gas would accumulate at the front of a steam chamber. This gas accumulation could slow down oil production as well as heat loss to the overburden. It is suggested that monitoring gas production during SAGD field operations may be critical for investigation of impacts of gas, and for developing strategies for performance improvement. Introduction It was generally believed that gas could have positive impacts on SAGD operation. The negative impacts of gas on SAGD operation have also been noticed, but usually considered minor [1–15]. Recently, however, theoretical and numerical study had shown that the negative impacts of gas on SAGD could be critical [16]. Two lab scale experiments concerning this issue have been conducted at Alberta Research Council. One was with dead oil and the other was with live oil. Results of these experiments were then history matched and analyzed using CMG's thermal reservoir simulator STARS. In this report, we summarize the results from the study. EXPERIMENTAL The physical model was a rectangular stainless steel cell of 80 cm in length, 24 cm in height and 10 cm thick. The cell was packed with 220 Darcy Ottawa sand saturated with water and bitumen. The production well was placed at the centre across the length, 2.2 cm above the bottom of the cell and parallel to the thickness of the cell. The injection well was placed 5 cm above the production well. The cell was carefully wrapped with multiple layers of Nomax insulation. Two layers of Nomax and one 1.9 cm thick plywood were used for the front wall and for the back wall. The heat transfer coefficient of one layer of Nomax was 0.08184 J/cm2- min- °C and that of one layer of plywood was 0.02684 J/cm2-min- °C. The cell was confined in a pressure vessel filled with nitrogen so that the overburden pressure of the cell was controlled. The viscosity of the dead oil used in the two tests was 32,500 cP at 15 °C. In the dead oil experiment, the initial cell pressure was 2,168 kPa and the initial cell temperature was 22 °C. The sandpack porosity was 36.7%. The initial oil saturation was 87%. The steam was slightly superheated and injected at an average rate of 33 cc/min, except during the first 10 min. During the first 10 min, steam was circulated into the injection and production wells, with the steam injection rate being at an average value of 68 cc/min. The experiment lasted 450 min. In the live oil experiment, the initial cell pressure was 2,184 kPa and the initial cell temperature was 22 °C. The sandpack porosity was 36.3%. The initial oil saturation was 89%. The live oil was prepared by saturating dead oil with methane, GOR of about 8. The steam was slightly superheated and injected at an average rate of 35 cc/min.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.996

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)0.0050.000

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 head0.008
GPT teacher head0.197
Teacher spread0.189 · 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.

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
Published2003
Admission routes1
Has abstractyes

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