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Record W1971081106 · doi:10.2118/2008-139

How Fast is Solvent Based Gravity Drainage?

2008· article· en· W1971081106 on OpenAlex

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

VenueCanadian International Petroleum Conference · 2008
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Numerical Methods
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsDrainageComputer scienceGeologyEnvironmental sciencePetroleum engineeringEcology

Abstract

fetched live from OpenAlex

Abstract A new correlation has been developed to predict bitumen/heavy oil production rates for solvent based gravity drainage. The correlated data set includes 60 individual rate measurements with both published and previously proprietary experimental data. The data set includes 11 crude oils, 4 solvents, permeabilities between 1.5 and 5,400 Darcies, viscosities between 90cP and 800,000 cP, condensing and non-condensing conditions. The oil production rates in the data set span a 30,000 fold range. The correlation only requires the raw bitumen viscosity and a grain (or pore) size parameter and provides a correlation coefficient R2 of 0.974 (R2=1 indicates a perfect correlation). Consequently, the correlation appears to be extremely robust and captures the full functionality of the solvent based gravity drainage extraction mechanism. The functionality of the correlation is consistent with both laboratory observations and mass transfer theory but contradicts and invalidates many of the assumptions of analytical and numerical models. The correlation suggests a common rate limiting step in both N-Solv and VAPEX and consequently, it is expected that this same rate limiting step may also apply to solvent- steam hybrid processes. The correlation resolves the long standing discrepancy between Hele-Shaw and packed bed experiments and provides a useful technique to assess the quality and reliability of experimental data and identify outliers. The correlation also provides an independent test to validate numerical models of solvent extraction. Most importantly, the correlation can help provide a basis to identify and rank extraction processes with target reservoirs. Introduction The Canadian tar sands are an enormous energy resource containing 1.6 to 2.5 trillion barrels of hydrocarbon liquids. More than 90% of the hydrocarbon resource lie in deposits that are too deep to mine economically. Steam Assisted Gravity Drainage (SAGD) is currently the most popular process for in situ extraction. SAGD uses twin horizontal wells in the pay zone, the upper wellbore is used to inject high pressure steam, while condensed water and "melted" bitumen drains via gravity and is collected in the lower wellbore. Gravity drainage is a very important technology advance as it can help avoid the bypassing (short circuiting) problem inherent in steam drive floods. Steam extraction requires heating about 8 kg of tar sand to a very high temperature (200–260 °C) to mobilize 1 kg of bitumen. Consequently, steam production requires combustion of enormous amounts of fuel (perhaps 30% of the heating value of the bitumen) and creates substantial carbon dioxide emissions. The subsequent upgrading of the raw bitumen to synthetic crude oil generates additional carbon dioxide emissions, along with volumetric shrinkage of about 15% due to coke rejection. The gross CO2 emissions just to produce a cubic meter of synthetic oil derived from SAGD bitumen can exceed 1000kg. A challenging issue for SAGD is that of chamber confinement. Virtually every SAGD project is operated at highly over pressured conditions relative to the original reservoir pressure. This enhances the heat transfer by driving steam into the bitumen and helps displace non-condensable gases from the chamber.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.658
Threshold uncertainty score0.956

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 head0.028
GPT teacher head0.240
Teacher spread0.212 · 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