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Record W1993700507 · doi:10.2118/100467-ms

Polymer Reduction Leads to Increased Success: A Comparative Study

2006· article· en· W1993700507 on OpenAlexaffabout
Natasha Kostenuk, P. J. Gagnon

Bibliographic record

VenueSPE Gas Technology Symposium · 2006
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsARC Resources (Canada)
Fundersnot available
KeywordsFracturing fluidPolymerMaterials scienceFracture (geology)ViscosityComminutionPetroleum engineeringComposite materialGeologyMetallurgy

Abstract

fetched live from OpenAlex

Abstract Recent advances in guar and cross-linker technologies have resulted in the development of high viscosity cross-linked borate fracturing fluids without increasing polymer loadings. These Low Polymer borate fracturing fluids (LP) are successfully being utilized in various formations previously believed to be too hot and or too deep for low polymer fracturing fluids. Historically, polymer loadings of 3.6 – 4.2 kg/m3 (30-35 lb/1000gal) were commonly pumped in the Western Canadian Sedimentary Basin (WCSB) for formations deeper than 2500 meters and bottom hole temperatures greater than 80°C. These same formations are now fracture stimulated using the Low Polymer fluids with loadings as low as 1.8 kg/m3 (15 lb/1000gal) with exceptional results. This paper demonstrates that Low Polymer fracture fluids can be used in place of higher polymer fluids with minimal changes to the overall design of the fracture treatment. The new fluid can be pumped on-the-fly at conventional pump rates and proppant concentrations due to the fluid's improved shear and temperature stability. The advantages of using a reduced polymer fracturing fluid include increased production, lower treatment costs, and lower friction pressures. This paper illustrates these advantages as it compares the Low Polymer fracture fluid with High Polymer fracture fluids in over 200 wells in the WCSB. The formations where LP fluids were utilized have depths of up to 3250 meters and reservoir temperatures reaching over 100°C.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.006
GPT teacher head0.233
Teacher spread0.227 · 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 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

Citations5
Published2006
Admission routes2
Has abstractyes

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