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Record W1993142898 · doi:10.2118/165477-ms

SAGD Pad Performance in an Ultra-Defined Athabasca Point Bar Deposit

2013· article· en· W1993142898 on OpenAlexaff
Yi Su, Jingyi Wang, Ian D. Gates

Bibliographic record

VenueSPE Heavy Oil Conference-Canada · 2013
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBar (unit)Steam-assisted gravity drainagePoint barPoint (geometry)Position (finance)Orientation (vector space)Process (computing)GeologyOil sandsGeometryComputer scienceMaterials scienceMathematicsComposite materialStructural basin

Abstract

fetched live from OpenAlex

Abstract It has been shown that the performance of a Steam-Assisted Gravity Drainage (SAGD) well pair is affected by its orientation and position within a point bar. In typical commercial operations, multiple wellpairs, usually arranged parallel to each other, are arranged in pads within oil sands reservoirs. Thus, the overall performance of the recovery process in a point bar is not that of a single well pair but reflected by oil accumulation differences, interactions between well pairs (pressure and fluid flow), and how the well pairs interact with the structure and geometry of the point bar including the arrangement of inclined heterolithic strata relative to the SAGD well pairs. This research describes how the point bar structure impacts the performance of a pad of SAGD wellpairs and the impact of pad orientation on performance of the pad. The results show that the performance of well pairs in a SAGD pad are affected by the orientation of the pad within the point bar. Also, the results show that the variability of the performance of the well pairs within the pad is large and thus, single well pair models do not provide sufficient analysis of process performance due to the heterogeneity of the point bar. In other words, pad-scale models are required for recovery process evaluation and design.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
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)0.0010.000

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 head0.009
GPT teacher head0.183
Teacher spread0.174 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

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