An Effective Stimulation Fluid for Deep Carbonate Reservoirs: A Core Flood Study
Bibliographic record
Abstract
Abstract Matrix acidizing is used to remove near wellbore damage and create channels or wormholes in carbonate formations to improve well performance. The use of conventional matrix acidizing fluids with HCl is not effective in some cases because of the rapid acid spending. Previous studies have demonstrated the use of chelates such as ethylenediaminetetraacetic acid (EDTA) and N-hydroxyethylenediaminetriacetic acid (HEDTA) as alternatives for HCl to stimulate carbonate reservoirs. A recently introduced chelating agent was examined to stimulate deep carbonate reservoirs. This chelating agent can be used at very low injection rates to avoid fracturing the target zone during the treatment, which may occur if HCl is used at high flow rates. The chelating agent used in this study was glutamic acid-N, N-diacetic acid (GLDA). Two sets of calcium carbonate cores were used one with 1.5 in. diameter and 20 in. length and the other set was 1.5 in. diameter and 6 in. length. Calcium carbonate cores such as Indiana limestone cores were used in this study. A dolomite core 1.5 in. diameter and 6 in. length was used to investigate the ability of this chelating agent to stimulate dolomite cores. The cores were treated with GLDA at various pH (1.7–13) and temperatures (180–300°F). The concentrations of dissolved calcium, magnesium, and GLDA in the core effluent were measured for material balance determination. GLDA was found to be highly effective in creating wormholes over a wide range of pH (1.7–13) in calcite cores. Increasing temperature enhanced the reaction rate, more calcite was dissolved, and larger wormholes were formed for different pH with smaller volumes of GLDA solutions. In addition, GLDA was very effective in creating wormholes in the dolomite core as it is a good chelate for magnesium. GLDA was found to be equally effective in creating wormholes in short and long cores.
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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".