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Record W2310645682

Investigation of Enhancing Drill cuttings Cleaning and Penetration Rate Using Cavitating Pressure Pulses

2014· dissertation· en· W2310645682 on OpenAlexafffund
Sadegh Babapour

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2014
Typedissertation
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsMemorial University of Newfoundland
FundersMemorial University of NewfoundlandAtlantic Canada Opportunities AgencyResearch and Development Corporation of Newfoundland and LabradorSuncor Energy Incorporated
KeywordsVenturi effectCavitationMechanicsTurbulencePressure sensorHydrostatic pressureMaterials scienceDrillMechanical engineeringFluid dynamicsEngineeringGeotechnical engineeringPhysics
DOInot available

Abstract

fetched live from OpenAlex

Drilling efficiency is governed by rock cuttings removal by hydraulic forces. The mechanical force introduced by the drill bit removes the rock chips from the parent rock. The chips will be held down until the downward forces due to overburden pressure are overcome. The turbulent jet that flushes away these chips consists of static impingement and dynamic pressure fluctuations. Instead of providing high pressure and hence enhancing the pressure fluctuations of the turbulent jet by rig pumps, the existing fluid pressure can be used more effectively. A fluid passing a Convergent-Divergent venturi demonstrates significant pressure fluctuations due to the cavitation phenomenon. As the fluid passes the vena-contracta, according to the Bernoulli’s principle, the fluid velocity increases and hence the pressure decreases. If pressure drops below the fluid vapor pressure, cavitation occurs and bubbles are created. Different prototypes were designed to investigate the probability of cavitation occurrence by using CFD simulations. The successful designs were venturis with diameters of 4 mm and 12 mm. Simulation software applies tetrahedral meshing to the prototype geometry for robust simulation results when geometry of the tool is complex. The results obtained confirmed the pressure pulses and occurrence of cavitation. An experimental setup consisting of a 12 mm venturi, two pressure sensors at upstream and downstream, and 3 load cells in a triangular combination, and a flow meter was used. The flow rate range was from 10 USGPM to 70 USGPM. The cavitation started at 25 USGPM with a shear noise that is the characteristics of a iii cavitating flow and the sensors recorded the pressure pulses at this point. The magnitude of pressure peaks ranged from 150 psi up to 600 psi. The second stage of the experiments was to investigate the effect of venturi and axial compliance in drilling. Compliant element used in these experiments consists of two plates with rubber mounts embedded between these two plates in an equilateral configuration. The rubber mounts enable the displacement of the upper plate on the base plate. An 8 mm venturi was also mounted on the drill string behind the bit as the vibration source. The experimental results show that the tool starts to cavitate and produce vibrations. The tool was tested with compliance and without compliance to seek the effects of the compliant element. Results show that when rigid (no compliance), the vibrations produced, did not have any significant effect on the rate of penetration (ROP). However, with integration of the compliant element, the vibrations produced by the tool intensified the natural vibration of the compliant element and the penetration rate increased.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.021
GPT teacher head0.244
Teacher spread0.224 · 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
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

Citations14
Published2014
Admission routes2
Has abstractyes

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