Time-Depth Curve Evaluation Method for Conversion Time to Depth at Penobscot Field, Nova-Scotia, Canada
Bibliographic record
Abstract
Oil and natural gas as a fossil fuel that is essential for human civilization, and included in nonrenewable energy, making this energy source is not easy for updated availability. So that it is necessary for exploration and exploitation reliable implementation. Seismic exploration becomes the method most widely applied in the oil, in particular reflection seismic exploration. Data wells (depth domain) and seismic data (time domain) of reflection seismic survey provides information wellbore within the timescale. As for the good interpretation needed information about the state of the earth and is able to accurately describe the actual situation (scale depth). Conversion time domain into the depth domain into things that need to be done in generating qualified exploration map. Method of time-depth curve to be the method most preferred by the geophysical interpreter, in addition to a fairly short turnaround times, also do not require a large budget. Through data information check-shot consisting of the well data and seismic data, which is then exchanged plotted, forming a curve time-depth curve, has been able to produce a map domain depth fairly reliable based on the validation value obtained in the range of 54 - 176m difference compared to the time domain maps previously generated.Keywords: Energy nonrenewable, survei seismik, peta domain waktu, peta domain kedalaman, time-depth curve
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".