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Record W3128122287 · doi:10.2118/0221-0029-jpt

Micro-Slimtube Shrinks EOR Screening From Months to a Week

2021· article· en· W3128122287 on OpenAlexaboutno aff
Trent Jacobs

Bibliographic record

VenueJournal of Petroleum Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsMicrofluidicsFluidicsPetroleum engineeringTest (biology)EngineeringMechanical engineeringNanotechnologyMaterials scienceElectrical engineeringGeology

Abstract

fetched live from OpenAlex

Oilfield testing firm Interface Fluidics says it is one step closer to reinventing the industry’s pressure-volume-temperature (PVT) testing portfolio after the development of a smaller, faster version of yet another laboratory stalwart. Representing the newest alternative to the slimtube test is the micro-slimtube test. A conventional slimtube test involves flowing gas through a sand- or glass-bead-packed metal coil that may be 1 to 4 mm wide and some 40 to 80 ft long to see how it mixes and mobilizes oil with samples also inside the tube. The test and subsequent analysis usually take a few months to complete. For a generation, this has been considered time well spent by anyone preparing to invest millions of dollars to prop up an aged asset through gas-injection-based enhanced oil recovery (EOR). But the times are changing. Interface Fluidics’ innovation, which it developed in close partnership with Equinor, measures only about 1.5 in long and generates results in about a week - about 95% sooner than the conventional bench method. The new test also reduces costs by around 75% while using a reservoir fluid sample that’s 99% smaller (10 ml vs. 1 liter). “It’s the same story over and over again - we’re miniaturizing the big stuff and putting it on a chip,” said Stuart Kinnear, CEO of Interface Fluidics. Founded in 2016, the Calgary-based firm helped introduce microfluidic technology to the oil industry with glass and silicon chips that it calls “reservoir analogues.” A well-established enabler in the healthcare industry, microfluidic devices of various stripes are routinely used to rapidly screen new drugs or to study how blood cells move through tiny veins and capillaries. In the upstream industry, Interface Fluidics is part of much smaller group of specialists proving that the devices are also ideal for screening production-enhancing chemicals and to study how oil moves about the tiny pathways of a reservoir rock (SPE 188895). The firm first showed how this works by replicating reservoir rock samples onto its chips as an alternative to core flood experiments. For oil and gas producers and their chemical providers alike (SPE 189780), the lower-cost devices made it affordable to run dozens of tests to determine how various chemistries affect flow behavior in specific geologies.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.250
Teacher spread0.237 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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