The Research and Application of New Low Density Nitrogen Micro Bubble Workover Fluid
Bibliographic record
Abstract
Aiming at the need of coiled tubing sand washing operation for the low-pressure and leaking gas pool in Qinghai gas reservoir, we developed a kind of low density nitrogen micro bubble workover fluid technology, which is composed of a new type of composite foaming agent, temporary plugging agent, composite stabilizing foam agent and the nitrogen. Observing by a microscope, we can find that the micro bubble structure is composed of a core of gas, two membranes, three layers, which has high stability (higher stability than ordinary foam stability). The performance evaluation results show that, the temperature resistance of nitrogen micro bubble workover fluid is up to 120 ℃. Compression resistance can reach 20 Mpa. API filter loss is only 11.3 ml. The high temperature and high pressure filtration is only 16.6 ml. Anti-salt can reach 10%. Anti-calcium can reach 3% and the resistance to oil pollution is more than 15%. The recovery rate of core permeability is up to 89%. The system can not only reduce the fluid column pressure and reduce the pressure difference, but the formation of micro bubble in the leakage areas on the surface of formation is widespread, and has a certain strength and toughness, and also has a certain deformability matching leakage channel of formation, which can achieve the purpose of anti-leaking. This technology was applied in Qinghai gas field for 2 wells, with an efficiency of 100%. The sand washing operation was successfully completed, with no leakage. Key words : Low density nitrogen micro bubble workover fluid; Micro bubble; Qinghai gas field
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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 | 0.000 | 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 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".