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Record W4252893418 · doi:10.2523/77408-ms

Low Field NMR Water Cut Metering

2002· article· en· W4252893418 on OpenAlexaffabout
I. P. Wright, Lastockin David, Allsopp Kevin, Evers-Dakers Maureen, Apostolos Kantzas

Bibliographic record

VenueProceedings of SPE Annual Technical Conference and Exhibition · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of CalgaryCanadian Natural Resources
Fundersnot available
KeywordsMetering modeEnvironmental scienceWater qualityVolume (thermodynamics)Materials scienceAsphaltPetroleum engineeringProcess engineeringGeologyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

We have developed a new on line water cut meter using low field Nuclear Magnetic Resonance (NMR) technology. This instrument is designed for use on heavy oil systems where conventional instruments experience difficulties.We present laboratory and field data for application of low field NMR to water cut measurements of bitumen/water mixtures. Data from successful field tests near Cold Lake, Alberta, Canada, shows that the instrument is capable of making water cut measurements over a wide range of fluid types and temperatures. We have successfully measured fluid streams with temperatures ranging from 60 to 150 degrees Centigrade and with water cut ranging from 40 to 95 percent. The instrument is capable of functioning accurately over a wide range of emulsions and/or foams and through significant variations of water salinity.The current application is for water cut measurements on a well site. However, the instrument can be applied in any system where heavy oil, bitumen, water, gas and solid systems may be encountered. It can be used for water cut and/or for three phase (oil/water/gas) volume fraction measurements. The instrument is equally capable of performing well site monitoring for regulatory/reconciliation purposes, for characterizing produced fluids, in separation, pipelining and upgrading processes for process control and for quality testing.

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 score0.143
Threshold uncertainty score0.896

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)0.0010.000

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 head0.017
GPT teacher head0.281
Teacher spread0.264 · 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

Citations0
Published2002
Admission routes2
Has abstractyes

Explore more

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