Optimization of 3-D Gas Permeability Measurement: Utilization of Field Data
Bibliographic record
Abstract
Abstract Gas permeability measurement is introduced to replace liquid permeability that has been associated with several complications and thus poses serious concerns. The resultant permeability value is an average property of the whole sample based on Darcy's approach. The Automated Probe Gas Permeameter (APGP) is gaining greater acceptance in lab and field applications for its simplicity and flexibility in measuring permeability at any petite spot on a sample surface and the ability to take measurements from irregular-shaped/different-sized samples at relatively short time intervals as compared to conventional techniques. The main concerns regarding the minimum sample size that can sustain permeability measurement, the question of how far the permeameter probe should be placed away from the boundary of the sample and the optimum size of the probe raise many doubts about the fate of this technology. The lab program utilized five standard Berea sandstone samples, three carbonate samples retrieved from currently producing oil reservoirs and one outcrop limestone sample. Obtained data have been analyzed using a designated regression package of modeling variograms. An analysis of bivariate modeling has been used to relate measured permeability to petrophysical properties of samples; mainly porosity, bulk permeability, pore throat quality/distribution and fracture parameters, if any. Existed concerns about the use of the Automated Probe Gas Permeameter have been investigated. Results show a strong relation between sample size, lateral/axial radius of investigation, and measured permeability. Other petrophysical properties show interesting, but moderate relationships. Fractured and vugy samples should be treated very carefully in terms of deciding probe position and data interpretation. Employment of set criterion may dramatically increase the implementation of the Automated Probe Gas Permeameter and improve confidence in resulting data. Field utilization of the equipment enhances efficiency in decision making right on the spot.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".