Research on casing damage risk early warning technology based on MIT-MTT logging and 16S rRNA gene analysis
Bibliographic record
Abstract
With the continuous advancement of oilfield development, the number of oil-water wells experiencing casing damage due to corrosion has increased annually, which has seriously affected the injection-production balance and significantly reduced the potential for recovering residual oil. Because the water cut trends in casing-damaged wells are similar to those in water breakthrough injection wells, identifying wells with casing damage has been challenging. This study is the first to combine the Multi-Finger Imaging Tool (MIT)-Magnetic Thickness Tool (MTT) integrated logging technology with 16S rRNA gene analysis to systematically analyze the relationship between casing damage and the composition of microbial communities. The results indicated that there were significant differences in the high-corrosion zones at various depths, and the structure of the sulfate-reducing bacterial community in the produced fluids also varied. In particular, in deeper zones, the relative abundance of thermophilic bacteria, represented by Thermotogata, increased significantly. Moreover, the more severe the casing damage, the more dominant the sulfate-reducing bacteria became in the microbial community of the produced fluids. After secondary sealing treatment, the proportion of sulfate-reducing bacteria was significantly reduced. The study further found that sulfate-reducing bacteria primarily belonged to the phyla Proteobacteria, Bacillota, Thermotogata, and Thermodesulfobacteriota, while significant populations of iron-reducing bacteria were not detected in the produced fluids. This finding suggests that sulfate-reducing bacteria are the main microbial factor causing metal corrosion of the casings. Innovatively, this study proposes a biometal corrosion monitoring method for production wells based on microbial community structure, thereby providing a novel technical approach to preventing oil well corrosion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".