DRY HOLE ANALYSIS PADA LAPANGAN PENOBSCOT, NOVA SCOTIA, KANADA DENGAN MENGGUNAKAN DATA SUMUR DAN DATA SEISMIK YANG TERINTEGRASI DENGAN PETROLEUM SYSTEM
Bibliographic record
Abstract
Lapangan Penobscot merupakan lapangan minyak dan gas yang termasuk dalam cekungan Scotia tepatnya di propinsi Nova Scotia, Kanada. Data seismik yang tersedia berupa 3D marine seismic, diakuisisi pada tahun 1969 dan hanya dipublikasi dua sumur yaitu L-30 dan B-41 (Opendtect.org). Dari dua data sumur yang dipublikasikan sumur L-30 ditemukan hidrokarbon berupa light oil (condensate) dan gas sedangkan sumur B-41 yang diyakini sebagai up dip dari sumur L-30 tidak ditemukan keterdapatan hidrokarbon (dry hole) . Dalam menganalisa dry hole pada sumur B-41 maka telah dilakukan korelasi dua data sumur yang menggunakan data log gamma ray, log spontaneous potential, log neutron density, dan depth. Dilakukan juga korelasi data seismik dan cross section antara data sumur dengan data seismik. Akibat adanya intrusi magma dari bawah permukaan berupa dike menyebabkan terbentuknya struktur sinklin yang menghalangi jalur migrasi hidrokarbon sehingga mengakibatkan sumur B-41 yang diyakini up dip dari sumur L-30 mengalami dry hole.Kata Kunci: korelasi data sumur, interpretasi data log, petroleum system, dry hole, dike
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 | 0.003 |
| 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) | 0.011 | 0.002 |
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".