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Record W2023188893 · doi:10.1088/0957-0233/25/3/035004

Region of interest selection of long core plug samples by magnetic resonance imaging: profiling and local <i>T</i><sub>2</sub> measurement

2014· article· en· W2023188893 on OpenAlexaff
Sarah Vashaee, Oleg V. Petrov, Bruce J. Balcom, Benedict Newling

Bibliographic record

VenueMeasurement Science and Technology · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of New Brunswick
FundersSaudi Aramco
KeywordsRegion of interestField of viewExcitationMagnetic resonance imagingOpticsMaterials scienceNuclear magnetic resonanceMagnetic fieldPhysicsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Magnetic resonance imaging (MRI) is increasingly employed as a core analysis technique by the oil and gas industry. In axial profiling of petroleum reservoir core samples and core plugs, the sample of interest may frequently be much longer than the natural field of view (FOV) defined by the radio frequency (RF) sensor and region of constant magnetic field gradient. Profiling such samples with a low field MRI will result in distorted, non-quantitative axial profiles near the edge of the FOV with data from outside the desired FOV folding back into the image, when the gradient magnetic field homogenity region is shorter than the region of RF excitation. The quality of MRI as a core analysis technique is increased if imaging can be performed on intact samples with the FOV reduced to the region of interest (ROI), either to increase the image resolution or to reduce the total time for imaging. A spatially selective adiabatic inversion pulse is applied in the presence of a slice selective magnetic field gradient to restrict the FOV to an ROI that is a small portion of a long sample. Slice selection is followed by a 1D centric-scan SPRITE measurement to yield an axial fluid density profile of the sample in the ROI. By employing adiabatic pulses, which are immune to RF field non-uniformities, it is possible to restrict the ROI to a region of homogeneous RF excitation, facilitating quantitative imaging. The method does not employ conventional selective excitation, but a subtraction based on images acquired with and without adiabatic inversion slice selection. The adiabatic slice selection lends itself to a selective T2 distribution measurement when a CPMG pulse sequence follows the slice selection. The inversion pulse selects a slice on the order of 1 cm at an arbitrary position. The local T2 distributions measured are of similar quality to bulk CPMG. This method is an alternative to MRI-based techniques for T2 mapping in short relaxation time samples in porous media when T2 is required to be measured at only a few positions along the sample, and a resolution of 1 cm is acceptable.

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)0.001
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 score0.230
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.034
GPT teacher head0.269
Teacher spread0.235 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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