X-RAY IMAGING TECHNIQUE SIMPLIFIES AND IMPROVES RESERVOIR-CONDITION UNSTEADY- STATE RELATIVE PERMEABILITY MEASUREMENTS
Bibliographic record
Abstract
This paper describes an x-ray imaging technique that has been successfully employed to simplify and improve one of the most basic (and potentially most error-prone) laboratory measurements required for calculating relative permeability functions from unsteadystate core flood data - namely the measurement of fluid production and saturation history. Typically, during conventional unsteady-state core tests, fluids that are produced from a core travel through some distance of tubing downstream from the core before reaching the device that measures or infers produced fluid volumes. Because production is normally measured downstream from the outlet face of the core, a number of corrections are necessary to compensate for upstream and downstream tubing volume, time lag between when fluids exit the core and when they are measured or observed, and any volume changes that occur because of differences in pressure and temperature at the point of measurement compared to core conditions. Measurement complexity and potential for errors increase when produced fluids do not separate quickly. Thus, potential sources of error include how data is corrected as well as how production is measured. Obviously, data errors yield imprecision in relative permeability results. By acquiring x-ray images of the entire core at various times during a coreflood using an x-ray image intensifier or similar type of device, one gains real time data describing bulk saturation changes within a core. This data is processed to yield volumetric data for input in conventional unsteady-state relative permeability calculations. Production data does not need to be time corrected as it directly reflects fluid saturation changes within the core plug during times that directly match those of pressure measurements. The technique greatly simplifies reservoir condition tests even when produced fluids separate slowly or are in the form of emulsions. Examples from reservoir condition tests are provided to demonstrate the technique.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".