Region of interest selection of long core plug samples by magnetic resonance imaging: profiling and local <i>T</i><sub>2</sub> measurement
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".