Comparison Study of Capillary Pressure Curves Obtained Using Traditional Centrifuge and Magnetic Resonance Imaging Techniques
Bibliographic record
Abstract
Abstract The current study is a comparison trial of a new Magnetic Resonance Imaging (MRI) method for acquiring capillary pressure versus traditional techniques. This study is meant to evaluate the methodology and workflow by performing measurements at the University of New Brunswick, Canada and on-site with CoreLab in Houston, TX, USA. This study focuses on gas-brine primary drainage capillary pressure systems. Traditional centrifuge capillary pressure measurements require the fluid(s) to reach equilibrium at many different speeds. This is very time consuming as each equilibrium step can take a couple of days. In addition, the inlet saturation must be computed using an approximate solution that is known to cause errors. Porous plate capillary pressure measurements are considered the most accurate but acquiring the complete curve can take months. The new method (GIT-CAP) centrifuges the core plugs then directly measures the water saturation distribution inside the core plug using MRI. The measured water saturation together with the known centrifugal force directly leads to a capillary pressure curve. Conventional medical based MRI methods have difficulty in relating the detected signal intensity to water or oil saturation. This is because the MRI image intensity depends on the environment of hydrogen atoms which changes based on saturation level. In this work, we use a new MRI method, one dimensional centric scan Single-point Ramped Imaging with T1 Enhancement (SPRITE), in which the detected signal is directly proportional to the amount of water or oil present. The new technique measures the capillary pressure curve more quickly and accurately. It is also three to five times faster since only two to three centrifuge speeds are required (versus seven to ten). In some rock types, this reduces the measurement duration from many weeks to days. The new technique is also potentially more accurate as it directly measures the water saturation in the rock instead of relying on a calculation using a measurement of the expelled water. The current study focuses on gas-water systems comparing traditional capillary pressure measurements with the new MRI-based method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".