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Record W1983710969 · doi:10.2118/59276-ms

Heavy-Oil Production Enhancement by Encouraging Sand Production

2000· article· en· W1983710969 on OpenAlexaboutno aff
Maurice B. Dusseault, Samir El-Sayed

Bibliographic record

VenueSPE/DOE Improved Oil Recovery Symposium · 2000
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringOil productionProduction (economics)GeologyEnvironmental scienceProduction rateOil fieldGeotechnical engineeringEngineeringProcess engineering

Abstract

fetched live from OpenAlex

Abstract Dramatic increases in oil production rates have been achieved in many Canadian heavy oil reservoirs. These reservoirs are 30% porosity unconsolidated sandstones with oil ranging from 500 to 12,000 cP viscosity. Furthermore, many of these reservoirs have proven to be almost impossible to exploit economically with horizontal wells or with thermal processes. After reviewing the mechanics of CHOP (Cold Heavy Oil Production), the production history of the Luseland Field in Saskatchewan is reviewed. This is almost a unique case history because conventional production, horizontal wells, and CHOP have all been attempted in a small geographic area. Encouraging sanding resulted in over a four-fold increase in oil production rate and a total extraction ratio now just in excess of 11% overall. An aggressive CHOP program implemented after many years of conventional production and after a six-well horizontal production program achieved this increase. Conventional production was marginally economic, but the horizontal wells were failures. Several other case histories are summarized to demonstrate that CHOP wells in these reservoirs are usually just as productive as much more costly horizontal wells. We believe that CHOP technology is a far better option than thermal stimulation or horizontal wells in many cases where the reservoir state and rock properties are suitable.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
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 communication0.0000.001
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 head0.004
GPT teacher head0.192
Teacher spread0.189 · 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 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

Citations29
Published2000
Admission routes1
Has abstractyes

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