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Record W2608905487 · doi:10.4043/27789-ms

Testing of a Novel Autonomous ICD with Low-Viscosity Multiphase Fluids

2017· article· en· W2608905487 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOffshore Technology Conference · 2017
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
FundersNational Institute of Standards and TechnologySouthwest Research Institute
KeywordsPetroleum engineeringLight crude oilViscosityMultiphase flowEnvironmental scienceVolumetric flow rateGas oil ratioVolume (thermodynamics)Materials scienceFlow (mathematics)Fossil fuelOil viscosityInflowWet gasAssociated petroleum gasProcess engineeringMechanicsNatural gasWaste managementChemistryThermodynamicsEngineering

Abstract

fetched live from OpenAlex

Abstract Applying proven technology to control the production of water and gas has become necessary to extend the life of very light-oil reservoirs while optimizing economics. Traditional inflow control devices (ICDs) can help balance the flow of oil, but are not helpful once water and gas breakthrough occurs. Multiphase data and field-evaluation applications show that low-viscosity, fluidic-diode, autonomous ICDs (AICDs) support the production of very light oil while restricting gas and water. Testing has proven that the low-viscosity, fluidic-diode AICD can differentiate oil from water and gas, even very light oils. Tool performance was characterized by measuring the pressure differential vs. the flow rate of diverse oil viscosities representing very light-oil formations in Canada, Russia, Malaysia, and Brazil. The AICD was flow tested with very light oils, water, and gas, as well as multiphase testing simulating mixtures of oil/water for different water cuts and oil/gas at diverse gas-volume fractions. The characterization of flow performance was embedded into sophisticated reservoir simulators for steady and transient evaluations. The multiphase condition of the test fluids was achieved by increasing water cuts and gas-volume fractions. The flow performance tests indicated that the highly sensitive fluidic sensor of the low-viscosity AICD enhances the production of very light oil and restricts water and gas as the water cut and gas-volume fraction increase. The restriction process gradually increases as per the water and gas ratio in the mixture and is reversible if water and gas production recede. Comparisons of the low-viscosity, fluidic-diode AICD vs. a traditional ICD show approximately 25% less water production and 40% less gas production with the AICD. The ability of the low-viscosity AICD to produce very light oils while restricting the flow of gas and water extends the life of light-oil reservoirs by increasing the production of hydrocarbons while helping to lower costs. For optimum reliability, this unique fluidic-sensor technology has no moving parts or control lines, but uses fluid dynamics to distinguish fluids. Multiphase-flow performance testing and field simulation of light-oil reservoirs indicate that the low-viscosity, fluidic-diode AICD favors the production of light oil (0.3 cP–1.5 cP) and restricts the flow of gas and/or water in a multiphase production-flow environment.

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.

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: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.721

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 science0.0010.000
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 head0.033
GPT teacher head0.281
Teacher spread0.248 · 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