Study on in situ measurement and analysis method of rock pores based on borehole camera technology
Bibliographic record
Abstract
In situ measurement and analysis of pores are helpful to understand the properties of rocks. However, continuous quantitative analysis of pores in whole borehole is still difficult. Borehole camera technology can capture the borehole wall image of the whole well section by going deep into the borehole, which provides technical conditions for the measurement and analysis of pores. In this paper, a continuous measurement method of rock pores based on borehole camera technology is introduced. According to the characteristics of the optical image of borehole wall, a method of pore recognition and analysis is proposed. Specifically, the influence of redundant information on pore recognition is eliminated, and then the pores are accurately recognized by binarization and morphological operation. Based on the results of pore recognition and the coordinate information provided by borehole wall image, the calculation methods of surface porosity and line porosity are proposed, and the statistical analysis of pore distribution is realized according to the calculation results. In addition, the morphological characteristics of pores are also discussed. This method realizes the accurate recognition and quantitative calculation of pore structure based on borehole wall image, and provides a new method for continuous analysis of pore structure in the whole well.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".