A new framework for selection of representative samples for special core analysis
Bibliographic record
Abstract
Special core analysis (SCAL) measurements play a noteworthy role in reservoir engineering. Due to the time-consuming and costly character of these measurements, routine core analysis (RCAL) data should be inspected thoroughly to select a representative subset of samples for SCAL. There are no comprehensive guidelines on how representative samples should be selected. In this study, a new framework is presented for selection of representative samples for SCAL. The foundation of this framework is using methods of PSRTI, FZI∗ (FZI-star) and TEM-function for the early estimation of petrophysical static, dynamic, and pseudo-static rock types at RCAL stage. The global hydraulic element (GHE) approach is benefitted and a FZI∗-based GHE method (i.e., GHE∗) is presented for partitioning data. The framework takes into consideration different laboratory, reservoir engineering, geological, petrophysical and statistical factors. A carbonate reservoir case is presented to support our methodology. We also show that the current forms of Lorenz and Stratigraphic Modified Lorenz Plots in reservoir engineering are not appropriate, and present new forms of them.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".