MétaCan
Menu
Back to cohort
Record W2030170218 · doi:10.1190/1.2135111

V P / V S characterization of a heavy-oil reservoir

2005· article· en· W2030170218 on OpenAlexafffundabout
Larry Lines, Ying Zou, Albert Zhang, Kevin Hall, Joan Embleton, Bruce Palmiere, Carl Reine, Paul Bessette, Peter W. Cary, Dave Secord

Bibliographic record

VenueThe Leading Edge · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsNexen (Canada)University of Calgary
FundersUniversity of Calgary
KeywordsPetroleum engineeringCharacterization (materials science)Reservoir modelingEnvironmental scienceGeologyMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

This article demonstrates a VP/VS application for a heavy oil field near Plover Lake, Saskatchewan, where Nexen has applied both hot and cold production methods. Plover Lake Field is about 8 km east of the Alberta-Saskatchewan border and about 320 km north of the Canada-U.S. border. Oil sands of the Devonian-Mississippian Bakken Formation are found in NE-SW trending shelf-sand tidal ridges that can be up to 30 m thick, 5 km wide, and 50 km long. Overlying Upper Bakken shales are preferentially preserved between sand ridges. The Bakken Formation is disconformably overlain by Lodgepole Formation carbonates (Mississippian) and/or clastics of the Lower Cretaceous Mannville group. Since sandstones have larger S-wave velocities (and hence lower VP/VS ratios) than shales, VP/VS maps should help to identify thickening sand layers within the target zone. We also intend to examine changes within the reservoir due to cold production. Unlike the steam injection processes used in enhanced heavy oil recovery, cold production processes have low energy requirements and use progressive cavity pumps—essentially powerful augers that suck oil and sand from producing formations.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.489

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 head0.023
GPT teacher head0.234
Teacher spread0.211 · 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 designOther design
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

Citations22
Published2005
Admission routes3
Has abstractyes

Explore more

Same venueThe Leading EdgeSame topicSeismic Imaging and Inversion TechniquesFrench-language works237,207