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Record W4386971157 · doi:10.1115/omae2023-105113

Study of the Influence of Microwave Irradiation on Hard Formation Property Alteration Through Nondestructive/Destructive Tests and Drilling/Coring Operations

2023· article· en· W4386971157 on OpenAlexaff
Abdelsalam Abugharara, Salum Mafazy, Stephen Butt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCoringDrillingUltimate tensile strengthPoisson's ratioPetroleum engineeringElasticity (physics)Materials scienceGeologyComposite materialPoisson distributionMetallurgy

Abstract

fetched live from OpenAlex

Abstract Enhancing the Drilling Rate of Penetration (D-ROP) is a target for lowering the ultimate cost in reaching hydrocarbon reservoirs, evaluating reservoir formation, and extracting minerals and reducing rock crushing consumed energy through mining operations. It has been reported that ROP can be positively influenced depending on optimization of several factors such as drilling parameters alteration and rock fragmentation process. In this research, D-ROP is evaluated by altering the status of the formations being drilled by the use of Microwave Irradiation (MI) while all other applied parameters are kept constant. Unlike published works, this research collectively investigates the influence of MI on D-ROP of hard formation providing confirmation on rock property alteration through non-destructive tests including ultrasonic wave velocities measurement and destructive tests including Indirect Tensile Strength (ITS). Results show gradual alteration in rock properties as per MI exposure time including a reduction in P-wave and S-wave velocity, a decrease in Young’s Modulus of elasticity (E) as well as Poisson’s ratio (v). Results also show formation fractures occur when exposed to MI, which lead to a decrease in the rock strength and ultimately lead to the increase of D-ROP.

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 score0.579
Threshold uncertainty score0.230

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 communication0.0000.001
Open science0.0000.000
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 head0.029
GPT teacher head0.252
Teacher spread0.223 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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