A novel method for digitizing sedimentological graphic logs and exporting into reservoir modeling software
Bibliographic record
Abstract
ABSTRACT Sedimentology is a key technical discipline in the energy and resource industry. One of its greatest benefits is in the detailed description and interpretation of full-diameter core as well as wellbore cutting samples. Such information adds significant value to hydrocarbon exploration and development by providing the basis for determining reservoir characterization and constructing precise subsurface stratigraphic models. Typically, such information is gathered in a hand-drawn format and/or produced in a computer-aided graphic illustration software format. Although this information is invaluable, it is hard to come by due to associated costs, and when available, it is limited to the above formats. The work presented herein proposes a novel approach to digitize the information contained within graphic logs. The digitized data are captured in a manner that allows it to be mapped into various other software. Hence, adopting such an approach provides unprecedented value in terms of harvesting sedimentological and petrographic data and integrating them into various other fields.
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 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 | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".