Development of efficient formulation for the removal of iron sulphide scale in sour production wells
Bibliographic record
Abstract
Abstract Iron sulphide scale, which exists in different forms, is common in sour oil and gas production wells. Iron sulphide hard scales are difficult to remove with acids, requiring mechanical intervention or the replacement of the production tubing. An environmentally friendly formulation with a high pH is proposed for the removal of both soft and hard iron sulphide scale from oil and gas wells. The formulation consists of DTPA (di‐ethylene tri‐amine penta acetic acid) in addition to K 2 CO 3 as a catalyst. High pressure high temperature solubility experiments were performed under both static and dynamic conditions in the temperature range of 70–150 °C and a constant pressure of 3447.38 kPa. Several combinations of the catalyst and DTPA chelating agent were used to optimize the catalyst/DTPA ratio to achieve maximum scale solubility. Field scale samples were collected and analyzed using XRD. The scale removal efficiency of the proposed formulation outperforms that of the current formulations used in the oil industry, with the added advantage of not releasing H 2 S. The optimum DTPA concentration is 20 wt% and the optimum catalyst concentration is 9 wt%, which provides a solubility of 90 % of the field scale. In addition, the ecotox profile of the proposed formulation is better than that of the currently used formulations because toxic corrosion inhibitors are not used. The maximum reported corrosion rate for the new formulation is 0.036 kg/m 2 , which is well below the acceptable limit (< 0.227 kg/m 2 ).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".