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Record W2308046828 · doi:10.2118/179761-ms

Phased Heating and Solvent Injection to Enhance Recovery of Heavy Oil and Bitumen

2016· article· en· W2308046828 on OpenAlexaff
Da Zhu, Gary L. Bunio, Ian D. Gates

Bibliographic record

VenueSPE EOR Conference at Oil and Gas West Asia · 2016
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of CalgarySuncor Energy (Canada)
Fundersnot available
KeywordsSteam-assisted gravity drainageAsphaltOil sandsSteam injectionSolventPetroleum engineeringWaste managementViscosityEnhanced oil recoveryEnvironmental scienceSynthetic crudeCarbon dioxideCombustionChemical engineeringChemistryMaterials scienceUnconventional oilFossil fuelGeologyOrganic chemistryEngineeringComposite material

Abstract

fetched live from OpenAlex

Abstract The main challenges faced by oil sands operators are the cost of operations and the environmental intensity of the recovery processes. The Athabasca oil sands deposit contains bitumen with viscosity typically over 1 million cP. To lower the viscosity of the bitumen so that it can be drained from these reservoirs, it is heated with injected steam by using Steam-Assisted Gravity Drainage (SAGD). This process is effective and enables recovery factors over 60%. The major cost in the recovery process is steam generation and associated water treatment and handling. The combustion of natural gas to generate steam is the main origin of the carbon dioxide emissions associated with SAGD. An alternative to steam injection is the use of solvents co-injected with steam. Solvents dilute bitumen leading to an oil phase with reduced viscosity. Also, there is potential to recycle solvent for re-injection. Thus, solvent added to steam can improve the steam-to-oil ratio and as a consequence can lower the carbon dioxide emissions per unit volume oil produced. In this extended abstract, we describe a phased solvent and heat process that yields improved performance beyond that of SAGD and current solvent-aided SAGD processes.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.631

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 head0.009
GPT teacher head0.242
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 designBench or experimental
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
Published2016
Admission routes1
Has abstractyes

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