Stimulation of Carbonate Reservoirs Using GLDA (Chelating Agent) Solutions
Bibliographic record
Abstract
Abstract The objective of matrix acidizing process is to create channels through the damaged zone in the near wellbore. Creating channels through the damaged zone yields a negative skin and improves the recovery in oil and gas reservoirs. The use of conventional matrix acidizing treatments with HCl is not effective in some cases due to corrosion and rapid acid spending at high temperatures. Previous studies have demonstrated the use of ethylenediaminetetraacetic acid (EDTA), hydroxy ethylethylenediaminetetraacetic acid (HEDTA), and DTPA (diethylenetriaminepentaacetic acid) as alternatives for HCl to stimulate carbonate reservoirs. These chelating agents were tested only on short cores (less than 5 in. length). GLDA (L-glutamic acid-N, N-diacetic acid) chelating agent was introduced that can be used as an effective stimulation fluid for carbonate reservoirs. Unlike HCl, GLDA can be used at very low injection rates and can create wormholes without face dissolution problems or washout. Calcium carbonate cores 1.5 in. diameter of 6 and 20 in. lengths were used in this study. The optimum conditions for the formation of wormholes were studied using core flood experiments. These conditions were: flow rate, temperature, concentration and pH. Other factors such as rock permeability and core length were also examined. GLDA was found to be very effective in creating wormholes at low injection rates and low to moderate pH values. Increasing temperature increased the reaction rate and more calcium was dissolved and larger wormholes were formed. Also, the optimum injection rate and GLDA concentration that should be used to minimize the volume of the fluid in the stimulation treatment were determined.
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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".