Effect of Lithology on the Flow of Chelating Agents in Porous Media during Matrix Acid Treatments
Bibliographic record
Abstract
Abstract Chelating agents such as GLDA, EDTA, and HEDTA have been used to stimulate calcite reservoirs as alternatives to HCl. HCl based stimulation fluids are very corrosive at high temperatures and should be loaded with many additives to reduce corrosion problems. In addition, the application of HCl can lead to face dissolution at low flow rates. GLDA chelating agent was used to stimulate calcium carbonate cores up to temperature of 300°F and at low rates without any face dissolution problems. The dissolution of dolomite by chelating agents has not been thoroughly investigated. Preliminary experiments with EDTA at ambient temperature revealed no significant dolomite dissolution. The dissolution mechanism is probably inhibited by the low stability of the magnesium chelate at that temperature. In this study, we will investigate the ability of GLDA (glutamic -N,N- diacetic acid) to stimulate dolomite cores as well as calcite cores. GLDA with different pH (1.7, 3 and 13) was used for this study. Dolomite and Indiana limestone cores with dimensions of 1.5 in. diameter, 6 and 20 in. length were used. The coreflood experiments were run at different flow rates and temperatures to determine the optimum rate at which GLDA solutions can create wormholes in both dolomite and calcite cores. Complete fluid analysis for the coreflood effluent was done to study the reaction of GLDA with both dolomite and calcite cores. GLDA was very effective in stimulating both dolomite and calcite cores at different pH levels over a wide range of temperatures (180, 250 and 300°F). There was no well defined optimum injection rate at which the amount of GLDA needed to create wormholes was minimum, instead a broad range of injection rates was found for which the amount of GLDA needed to breakthrough the core was minimum. Also, GLDA effectively chelated magnesium and calcium from dolomite cores. GLDA was stable up to temperatures of 300°F and the concentration of GLDA after the treatment was the same as that before the treatment, further confirming the thermal stability of GLDA at this temperature.
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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".