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Record W1939199724 · doi:10.1306/13371598st643565

Screening Criteria and Technology Sequencing for In-situ Viscous Oil Production

2013· book-chapter· en· W1939199724 on OpenAlexaff
Maurice B. Dusseault

Bibliographic record

VenueAmerican Association of Petroleum Geologists eBooks · 2013
Typebook-chapter
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOil sandsAsphaltGeologyResource (disambiguation)Steam-assisted gravity drainagePetroleum engineeringOil reservesUnconventional oilMining engineeringEarth sciencePetroleumGeochemistryArchaeologyPaleontologyOil shaleGeography

Abstract

fetched live from OpenAlex

Abstract On a worldwide basis, approximately 70 to 80% of the 9 to 10 trillion bbl original oil in place of viscous oil reserves (in-situ u > 100 cp) occur in unconsolidated sandstones, with high porosity and permeability. The remaining viscous oil reservoirs are hosted within fractured carbonates. Both types of viscous oil reservoirs are characterized by highly variable in-situ reservoir conditions. Different technologies are best for different lithostratigraphic and geometric conditions. New and emerging production and drilling technologies (i.e., horizontal wells, multilaterals, logging while drilling, and others), along with production-technology sequencing, allow tailoring of the drilling and production schemes to each specific reservoir considering the inherent geologic variability of these unconventional reservoirs. Geotailoring for viscous oil production generates a need for geoscience and engineering screening criteria.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.016
GPT teacher head0.256
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations10
Published2013
Admission routes1
Has abstractyes

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