A New Finding in the Interaction Between Chelating Agents and Carbonate Rocks During Matrix Acidizing Treatments
Bibliographic record
Abstract
Abstract During matrix acidizing, successful iron control can be critical to the success of the treatment. Iron (III) precipitation occurs when acids are spent and the pH rises above 1, which can cause severe formation damage. Chelating agents are used during these treatments to minimize iron precipitation. In this paper, we studied the effect of iron precipitation in acidizing operations. HCl solutions (5 - 20 wt%) containing 5,000 to 10,000 ppm of Fe3+ were used in these experiments. Biodegradable GLDA (glutamic-N, N-diacetic acid) was studied in the experiments. The effect of varying acid concentration and chelate-to-iron mole ratio was examined. Coreflood experiments were conducted on low permeability Indiana limestone (1 - 5 md) at 200°F. The cores were scanned after treatments using a CT scanner. The core effluent samples were analyzed for total iron and calcium concentrations using ICP-ES. A calcium ion-selective electrode was used to determine the concentration of free calcium ions, i.e. calcium ions not complexed by the chelate, in the core effluent samples. Results showed that the amount of iron recovered depended on both chelate-to-iron mole ratio and the initial permeability of the cores. Calcium is chelated along with iron, which limits the effectiveness of chelating agents to control iron (III) precipitation. Chelating agents are supposed to control iron now that calcium is also chelated, this amount should be accounted for. Acid solutions should be designed considering this important finding for more successful treatments. This paper will discuss the results obtained and give recommendations to enhance the effectiveness of these chemicals in the field.
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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) | 0.002 | 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".