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Record W1564097364 · doi:10.2118/174428-ms

Reactive Steam Assisted Gravity Drainage Oil Sands Recovery Process

2015· article· en· W1564097364 on OpenAlexafffund
Zeinab Khansari, Ian D. Gates

Bibliographic record

VenueSPE Canada Heavy Oil Technical Conference · 2015
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSteam-assisted gravity drainagePetroleum engineeringOil sandsSteam injectionAsphaltOil shaleBaffleGeologyEnhanced oil recoveryViscosityAPI gravityPermeability (electromagnetism)Environmental sciencePorosityWaste managementGeotechnical engineeringChemical engineeringChemistryMaterials scienceCrude oil

Abstract

fetched live from OpenAlex

Abstract To extract oil from oil sands reservoirs the techniques are required that reduce the viscosity from hundreds of thousands and several millions of cP to a point that it can flow. In Steam-Assisted Gravity Drainage (SAGD) process, injected steam releases its latent heat and reduces the viscosity of the oil. This oil, referred to as bitumen, encountered steam-oil reactions process leading to aquathermolysis and steam-rock reactions - geochemical reactions. Geochemical reactions are responsible for in situ gas production and alteration of produced water composition that can affect the reservoir pressure, porosity, permeability, bitumen viscosity and thus the recovery process. In this research it has been examined that how water composition changes during SAGD operation. A reaction model was defined expressing the geochemistry of SAGD within the formation and a reactive thermal reservoir simulation was built to represent reservoirs with different shale layer geometries at various distances from SAGD wellpair. For the first time, the results demonstrated that the produced water composition can be used to detect baffles and barriers and their distances from wellpair. The analysis of produced water can be used as a tool to monitor the process dynamics and understanding the reservoir heterogeneity.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.709
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.248
Teacher spread0.220 · 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 designNot applicable
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

Citations2
Published2015
Admission routes2
Has abstractyes

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