A CONNECTED DOMAIN IDENTIFICATION METHOD AND ITS APPLICATION IN QUANTITATIVE PICKUP OF CAVE INFORMATION USING ELECTRIC IMAGING LOGGING
Bibliographic record
Abstract
Abstract Solution caves are important fluid reservoir space in carbonate reservoir, and researching FMI images' caves connected domain labeling and extracting their information are meaningful. A high resolution color image can be obtained after data processing of FMI. After a series of processes which include image graying, median filtering and threshold segmentation for the color image, a binary image will be obtained which can reflect the characteristic of solution caves on the wall of a well. And on the image, caves are black spots which are labeled by same number. The labeling algorithm for image connected domain based on equivalence pair processing has the advantages of fast and no‐repeat labeling, which can eliminate equivalent pairs while labeling connected domain. The solution caves in the binary image can be marked from small to large number accurately by this arithmetic, in addition, the information of every connected domain including holes' size, grading factor, area of connected domains (areal porosity) and roundness can be extracted and processed. Using the labeled binary image can calculate porosity curve which reflects development degree of caves, and based on this curve the image can be divided into several layers. On this basis, the information distribution of areal porosity, holes' size, roundness and grading factor of every layer can be calculated easily. At last, all of these informations will be used to quantitatively evaluate the carbonate reservoir which has strong heterogeneity and lots of solution caves. And this work is also a helpful exploration for quantitative extracting of cave information from FMI images.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".