Liquids-Rich Resource Play Characterization Using Well Log Analysis Calibrated with Rock Properties from Drill Cuttings
Bibliographic record
Abstract
URTeC 1620306 This paper presents results of a study evaluating the accuracy of well log analyses calibrated with rock properties measured from core and drill cuttings compared to a calibration using rock properties from just drill cuttings. The study wells are producing from a liquids-rich resource play in the Permian Basin of West Texas, USA. The results of our comparative study suggest that, when core data are not available, an acceptable alternative approach is to substitute limited but selected rock properties that can be measured accurately from drill cuttings. While the preferred method is to use whole core, logs calibrated in our study using cuttings-derived rock properties compare favorably to those calibrated using core measurements. Further, the abundance of representative cuttings available from most wells combined with the small rock sample volumes required for accurate laboratory measurements ensure the practical applicability of this method. Finally, the laboratory techniques for measuring the selected cuttings-derived rock properties used in our calibration process are well established by most commercial laboratories, thereby making this alternative approach both technically viable and cost effective.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".