Missing sonic log prediction using convolutional long short-term memory
Why this work is in the frame
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Bibliographic record
Abstract
We propose a method to estimate missing sonic logs by using a bidirectional convolutional long short-term memory (bidirectional ConvLSTM) cascaded with a dropout layer and fully connected neural networks (FCNNs). We train the model on 177 wells from mature areas of the UK continental shelf (UKCS). We test the trained model on one blind well from UKCS, two wells from the Volve field in the Norwegian continental shelf (NCS), and one well from the Penobscot field in the Scotian shelf offshore Canada. The method takes into account the rock properties trend and the local shape of logs, and produces accurate prediction of sonic logs from gamma-ray and density logs with an addition of uncertainty estimations and without the need for applying rock-physics model intervally. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 1:50 PM Presentation Time: 1:50 PM Location: 221D Presentation Type: Oral
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it