Mineral Analysis of Dolomite Formation During Carbonate Acidizing Using Chelating Agents
Bibliographic record
Abstract
During carbonate acidizing, the reaction between hydrochloric acid (HCl) and carbonate minerals is particularly rapid, especially in high-temperature wellbore environments. Due to the swift reaction kinetics and the rapid consumption of acid, deep penetration is often limited, resulting in the formation of small wormholes and localized dissolution, which minimizes skin damage. To address these challenges, chelating agents have been introduced as an alternative for reacting with dolomite formations. Chelating agents, being slower-reacting acids, have demonstrated effectiveness in high-temperature environments. In this study, three chelating agents—HEDTA (Hydroxyethylenediaminetetraacetic acid), GLDA (L-glutamic acid diacetic acid), and EDTA (Ethylenediaminetetraacetic acid)—were employed to interact with Guelph dolomite core samples under high-pressure (1000 psi) and high-temperature (180°F) conditions. The reacted dolomite samples were subsequently analyzed for changes in various properties, including mineralogy, grain size distribution, porosity, and morphology. Mineralogical and grain size distribution analyses revealed that GLDA and HEDTA were effective in dissolving calcite, while EDTA demonstrated a higher effectiveness in dissolving ankerite. Additionally, mineral locking analysis indicated that GLDA and HEDTA successfully disrupted the bond between quartz and calcite, which may contribute to an increase in reservoir permeability.
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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) | 0.001 | 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".