Investigation of Acid-Induced Emulsion and Asphaltene Precipitation in Low Permeability Carbonate Reservoirs
Bibliographic record
Abstract
Abstract The increasing demand for energy has extended the development horizon towards relatively tighter formations all over the world. In Saudi Arabia, hydrochloric and organic acids have been extensively used to enhance well productivity or injectivity in low permeability formations. However, the use of these acids was associated with severe formation damage, which is attributed to acid/oil emulsions and/or asphaltene precipitation in some of the low permeability carbonate reservoirs. Consequently, a detailed study on different factors that influence the acid/oil emulsion and asphaltene precipitation mechanism was carried out for these reservoirs. Several compatibility studies were conducted using representative crude samples and different acid systems such as HCl and formic acid. The experiments were conducted at various temperatures up to 240°F using HP/HT aging cell for both live and spent acid samples, where some of the experiments included anti-sludge, iron control and demulsifier chemical additives. In addition, another set of experiments was performed in the presence of ferric ions (Fe3+). The total iron concentration in these experiments varied between 0-1,000 ppm. The results obtained from this study have revealed that the acid systems were not compatible with several representative oil field samples. The amount of asphaltene precipitation and the stability of formed emulsions increased significantly in the presence of ferric ions. Several wells have already been acidized and damaged prior to initiating this study. This paper discusses different tests conducted to identify, quantify and treat acid-oil emulsions/asphaltene precipitation in tight carbonate reservoirs. It also provides details of a special solvent treatment fluid recommended to revive dead wells which were damaged by acid-induced emulsion and asphaltene precipitation.
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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 | 0.000 |
| 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 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".