Experimental study on the corrosion of a downhole string under flue gas injection conditions
Bibliographic record
Abstract
Abstract Flue gas collection from steam generators and its utilization in enhanced oil recovery (EOR) can reduce CO2 emissions into the atmosphere and improve oil recovery efficiency. Under the environments of flue gas corrosion in oilfields, the effects of corrosion time, temperature, pressure, velocity, and concentrations of O2, SO2, H2O, and NaCl on corrosion rates of steels used for a downhole string were investigated through physical simulation experiments. The corrosion mechanisms were analyzed by component, and the morphology of the corrosion products tested by X‐ray diffraction (XRD) and scanning electron microscopy (SEM). In the gas phase, the corrosion rates of X70, P110, and N80 notably increase with temperature and O2 concentration. The corrosion rates first increase rapidly with pressure from 1.0 to 3.0 MPa and then remain largely stable. Meanwhile, the corrosion rates of X70, P110, and N80 in the liquid phase first increase and then decrease with temperature and reach maximum values at 90°C. The corrosion rates of X70, P110, and N80 increase notably with velocity and the concentrations of O2, SO2, H2O, and NaCl. The corrosion rate of 13Cr is considerably lower than those of N80, P110, and X70, which shows good corrosion resistance performance. To reduce the flue gas corrosion of a downhole string, the relative humidity of the flue gas should be lower than 0.7, the temperature of the flue gas in the wellbore should avoid the range between 80 and 100°C, the excess air coefficient of the boiler should be kept at a reasonable value to reduce the O2 content in the flue gas, and the flue gas should not be coinjected into wellbores with brine. The injection of flue gas is technically feasible considering the corrosion of downhole string.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".