The U-tube sampling methodology and real-time analysis of geofluids
Bibliographic record
Abstract
The U-tube geochemical sampling methodology, an extension of the porous cup technique proposed by Wood [1973], provides minimally contaminated aliquots of multiphase fluids from deep reservoirs and allows for accurate determination of dissolved gas composition. The initial deployment of the U-tube during the Frio Brine Pilot CO{sub 2} storage experiment, Liberty County, Texas, obtained representative samples of brine and supercritical CO{sub 2} from a depth of 1.5 km. A quadrupole mass spectrometer provided real-time analysis of dissolved gas composition. Since the initial demonstration, the U-tube has been deployed for (1) sampling of fluids down gradient of the proposed Yucca Mountain High-Level Waste Repository, Armagosa Valley, Nevada (2) acquiring fluid samples beneath permafrost in Nunuvut Territory, Canada, and (3) at a CO{sub 2} storage demonstration project within a depleted gas reservoir, Otway Basin, Victoria, Australia. The addition of in-line high-pressure pH and EC sensors allows for continuous monitoring of fluid during sample collection. Difficulties have arisen during U-tube sampling, such as blockage of sample lines from naturally occurring waxes or from freezing conditions; however, workarounds such as solvent flushing or heating have been used to address these problems. The U-tube methodology has proven to be robust, and with careful consideration of the constraints and limitations, can provide high quality geochemical samples.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".