Formation Damage Due To Iron Precipitation In Carbonate Rocks
Bibliographic record
Abstract
Abstract Iron precipitation during matrix acidizing treatments is a well-known problem. However, extensive literature review highlighted that no systematic study was conducted to determine where this iron precipitates, the factors that affect this precipitation and the magnitude of the resulting damage. In this paper, the effect of iron precipitation in the acidizing operations is studied. HCl solutions (5 – 10 wt%) containing 5,000 to 10,000 ppm of Fe3+ were used for these experiments. The effect of varying acid concentration, initial core permeability, core length, temperature, and flow rate was studied. Coreflood experiments were conducted on 6 and 20 in. long Indiana limestone cores over a wide range of permeabilities and up to 300°F. In these experiments, 0.5 PV of acid solution was injected. The cores were scanned after treatments using a CT scanner and cut to better determine the location of iron deposition. The core effluent samples were analyzed for iron and calcium concentrations using ICP-OES. Results showed a significant amount of iron precipitated on the injection face of the cores and the sides of wormholes, i.e. where the contact occurs between the acid and the rock, producing a minimal or no gain in the final permeability, which indicated severe formation damage. The damage increased with the increase of the amount of iron in solution. At higher temperatures and flow rates, the damage was significant. Core length didn't affect the degree of damage. This paper will discuss the results obtained and give recommendations on whether to use iron control agents in the field or not.
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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 | 0.001 | 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".