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Record W2078494812 · doi:10.2118/02-05-05

A Device and Method of Determining the Rheological Quality of Gelled Fracturing Fluids

2002· article· en· W2078494812 on OpenAlexaboutno aff
B. Tremblay, M. De Rocco, R. K. Ridley, Suman Singh, Ken Scott, David J. Browne, B. Lukocs, B. O apos Neil

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2002
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBody orificeHydraulic fracturingRheologyPetroleum engineeringMaterials scienceSettlingRheometryFracture (geology)ExtrusionFracturing fluidComposite materialGeotechnical engineeringGeologyMechanical engineeringEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

Abstract A method of determining the quality of cross-linked hydraulic fracturing gels in the field was developed at the Alberta Research Council. The method is based on the measurement of the pressure required to push the gel through an orifice. The measurement can be repeated several times in order to quantify transient changes in the rheology of the gel. The quality of the gel prepared in the field is quantified by plotting the extrusion pressure vs. shear rate at the orifice. This quality control technique was compared, for certain types of water-based and oilbased gels, to existing techniques based on shear rheometry. The comparison showed that the water-based gels we investigated could be characterized using the gel tester, but not with the existing technology. The oil-based gels we investigated could be characterized better using the existing technology. Introduction Hydraulic fracturing is a common method of enhancing formation Productivity(1). Polymer gels are often used as fracturing fluids in order to carry solid particles (proppant), such as sand, into fractures. The sand keeps the fractures open after the injection is stopped. In order to maximize the fracture width, the gel must be able to prevent the sand from settling within the well and transport it through the perforations. In addition, the gel must have the proper leak-off properties in order to keep the fracture open when the fluid is being injected. According to Ely(2), most fracture treatments fail due to the inability of a fracturing fluid to carry proppant for the duration of the treatment at in situ conditions of temperature and shear, and/or to properly degrade back to water after the treatment. The current field quality control practice is to:measure the viscosity of the ungelled polymer solution using either a Fann-35 rheometer or, more recently, a Brookfield PVS rheometer;do a lip test; andmeasure the vortex closure time. Water-based fracturing gels are commonly used worldwide since they have the following advantages according to Ely et al.(3); 1) they are economical compared to oil, condensate, and methanol; 2) they yield increased hydrostatic head compared to oil, gases, and methanol; 3) they are incombustible; and 4) they are readily available. Hydrocarbon-based gels are used where the formation may be sensitive to water injection. As described in a monograph on hydraulic fracturing(4), computerized fracturing simulators normally require the consistency and flow behaviour indices to calculate the pressure drop along injection wells and within fractures. These parameters are obtained by fitting shear stress vs. shear rate measurements to a power law. The measurements are made using shear rheometers, such as the Fann-35, Fann-50 or the Brookfield PVS rheometer. It is difficult to measure the "viscosity" of certain cross-linked gels. The problem is that the gel slips on the walls of the bob and cup of the shear rheometers, as was observed by Cameron et al.(5) invisualization experiments showing the flow of coloured tracer particles within the gap.

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)0.003
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.252
Teacher spread0.232 · 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
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

Citations2
Published2002
Admission routes1
Has abstractyes

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