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Record W1999063680 · doi:10.2118/164048-ms

Novel Scale Remediation for Steam Assisted Gravity Drainage (SAGD) Operations

2013· article· en· W1999063680 on OpenAlexaffabout
Timothy Cheung, Michael Scheck

Bibliographic record

VenueSPE International Symposium on Oilfield Chemistry · 2013
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsSteam-assisted gravity drainagePetroleum engineeringSteam injectionBrineSpark plugEnvironmental scienceDifferential pressureDrainageEnvironmental remediationWaste managementOil fieldEnvironmental engineeringPulp and paper industryGeologyAsphaltChemistryOil sandsEngineeringMaterials scienceContaminationMechanicsMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Shell runs a Steam Assisted Gravity Drainage (SAGD) operation at the Orion field in Alberta, Canada. Pressure drops during bitumen and water recovery can lead to precipitation and buildup of scales which can plug up well equipment and lead to an increase in differential pressure and lower production. Early attempts to remove such blockages with high pressure steam or salinity-altering brine injections resulted in transient returns to normal operations but with diminishing returns. Acid treatments to dissolve the scale encountered problems with corrosion and an inability to handle the discovery of the presence of acid insoluble scales. Alterations to the operating conditions such as pH along with the addition of a chelating agent, EDTA, were successful in treating both types of scales. As a result, differential pressures and steam to oil ratios decreased while oil production grew. This treatment was extremely cost-effective and payout was on the order of a few days.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.008
GPT teacher head0.233
Teacher spread0.225 · 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 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

Citations7
Published2013
Admission routes2
Has abstractyes

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