Integration of completion data, microseismic data, downhole logs, and multicomponent seismic data in the Mississippi Lime, north-central Oklahoma
Bibliographic record
Abstract
Abstract Previously published work on a Mississippi Limestone (Mississippi Lime) prospect in north-central Oklahoma described a prospect that is data rich. Aquisition was on a dense grid using nodal 3C phones, and PP and PS data were both processed and jointly inverted. Several pilot holes and laterals were drilled and fully logged, including Sonic Scanner and formation-microimager (FMI) fracture logs. Microseismic data were acquired on one well pad, as was a 3D VSP. New resesarch discusses two specific fracture-characterization methodologies. The first is the integration of vertical and lateral log suites, including fracture imaging, with seismic rock properties and completion results, including microseismic data. The results of this effort not only characterize fracture width and height but go farther by explaining why and how those fractures are created. The second is the use of converted-shear (PS) data to measure anisotropy and correlate those measurements with fracture logs. Specifically, two methods of calculating anisotropy are analyzed: slow shear-wave (S2) traveltime correction and transverse/radial energy ratio. Both methods are theoretically valid but might break down in practice because of various nongeologic artifacts that can be present in the data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".