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Record W4238825755 · doi:10.4043/133453-ms

Numerical Simulation of Thermal Solvent Replacing Steam under Steam Assisted Gravity Drainage SAGD Process

2010· article· en· W4238825755 on OpenAlexaboutno aff
Weiqiang Li, D. D. Mamora

Bibliographic record

VenueSPE Western Regional Meeting · 2010
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSteam-assisted gravity drainageOil sandsAsphaltPetroleum engineeringSteam injectionSolventThermalEnvironmental scienceWaste managementMaterials scienceChemistryGeologyEngineeringThermodynamicsComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Steam Assisted Gravity Drainage (SAGD) is one successful thermal recovery technique applied in the Athabasca oil sands in Canada to produce the very viscous bitumen. Water for SAGD is limited in supply and expensive to treat and to generate steam. Consequently, we conducted a study into injecting high-temperature solvent instead of steam to recover Athabasca oil. In this study, hexane (C6) coinjection at condensing condition is simulated using CMG STARS to analyze the drainage mechanism inside the vapor-solvent chamber. The production performance is compared with an equivalent steam injection case based on the same Athabasca reservoir condition. Simulation results show that C6 is vaporized and transported into the vapor-solvent chamber. At the condensing condition, high temperature C6 reduces the viscosity of the bitumen more efficiently than steam and can displace out all the original oil. The oil production rate with C6 injection is about 1.5 to 2 times that of steam injection with oil recovery factor of about 100% oil initially-in-place. Most of the injected C6 can be recycled from the reservoir and from the produced oil, thus significantly reduce the solvent cost. Results of our study indicate that high-temperature solvent injection appears feasible although further technical and economic evaluation of the process is required.

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 categoriesMeta-epidemiology (narrow)
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 score0.345
Threshold uncertainty score1.000

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)0.0000.000

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 head0.018
GPT teacher head0.281
Teacher spread0.263 · 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 designSimulation or modeling
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

Citations1
Published2010
Admission routes1
Has abstractyes

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