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Record W1991027230 · doi:10.2118/86476-ms

Scale Control Within North Sea Chalk/Limestone Reservoirs. The Challenge of Understanding and Optimizing Chemical Placement Methods and Retention Mechanism: - Laboratory to Field

2004· article· en· W1991027230 on OpenAlexaff
M. M. Jordan, K. Sjursæther, I. R. Collins

Bibliographic record

VenueSPE International Symposium and Exhibition on Formation Damage Control · 2004
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsNalcor Energy (Canada)
Fundersnot available
KeywordsCarbonatePhosphonatePolymerScale (ratio)Environmental scienceChemical engineeringChemistryPetroleum engineeringGeologyEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The scale control challenges for two North Sea carbonate reservoirs are reviewed in this paper. Whilst carbonate reservoir are not the largest source of hydrocarbon within the North Sea, they are very significant on a global bases. The mechanism of scale inhibitor chemical retention observed for phosphonate, polymer, and vinyl sulphonate co-polymer inhibitors on carbonate reservoir substrates is outlined. Chemical placement represents the most significant technical challenge when performing scale squeeze treatments into fractured chalk reservoirs. Examples from over 50 field treatments applied in reservoirs E and V, where both phosphonate and vinyl sulphonate polymer chemicals have been deployed, are used to illustrate the difference in chemical retention observed in laboratory evaluations. The laboratory studies demonstrated clear potential for significant extension in treatment lifetime by changing from a phosphonate to a vinyl sulphonate co-polymer-based scale inhibitor. The selection and qualification of chemical placement systems for deployment of inhibitors in fractured carbonate reservoirs are also outlined. To this end, novel technologies to enhance conventional scale inhibitor chemical placement are vital to economic success during water flood projects.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.626
Threshold uncertainty score0.430

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 head0.013
GPT teacher head0.249
Teacher spread0.236 · 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 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

Citations29
Published2004
Admission routes1
Has abstractyes

Explore more

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