Evaluation of the effectiveness of mixing hydrochloric acid and organic acid as stimulation fluids in a tight gas carbonate reservoir
Bibliographic record
Abstract
Abstract Matrix acidizing is a widely used technique in carbonate formations to improve production and injection rates by restoring or improving permeability. This study evaluates the stimulation efficacy of organic and inorganic acid combinations in the Sarajeh carbonate formation, with a focus on improving permeability within this potential tight gas formation. Through solubility and continuous flow tests, a mixture of HCl 7.5% + HAc 2.5% was found to significantly outperform the traditional HCl 15% solution. The HCl 7.5% + HAc 2.5% mixture had a solubility rate of 59.27% and improved permeability by 4.4 times compared to HCl 15%, indicating a higher dissolving capacity. The results of continuous flow tests showed that the mixed acid had the best pore volume to breakthrough (PVBt) value of 2.574 at a controlled injection rate of 2 cc/min. This study demonstrates the superior performance of a lower concentration HCl‐HAc acid formulation in matrix acidizing carbonate formations and confirms its potential as a more effective alternative to high concentration HCl treatments for the Sarajeh reservoir.
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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 | 0.001 |
| 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) | 0.001 | 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".