Effect of Reservoir Fluid Type on the Stimulation of Carbonate Cores Using Chelating Agents
Bibliographic record
Abstract
Abstract Different fluids have been introduced in the oil industry to be used as alternatives to HCl. Chelating agents such as EDTA (ethylenediaminetetraaceticacid), HEDTA (hydroxyethylenediaminetriaceticacid), and GLDA (L-glutamic acid-N, N-diacetic acid) have been introduced to be used as stand-alone stimulation fluids. These fluids can be used to stimulate water injectors, oil, or gas producers, therefore, the effect of the type of reservoir fluid on the stimulation process should be investigated. In this study, 0.6M concentration of GLDA, EDTA, and HEDTA were used in the coreflood experiments on carbonate rocks at 300°F. The cores were saturated with water, oil, or gas to determine the effect of reservoir fluid type on the performance of the chelating agents with the carbonate cores. The different chelants were injected into the calcite cores saturated by oil and gas after flooding the water into the cores until the residual saturations were reached. The effect of using 10 vol% mutual solvent (ethyleneglycol-monobutyl-ether) in the preflush on the stimulation process was examined. CT was used to scan the cores after the treatments, and the 2D images were used to characterize the wormhole and face dissolution. GLDA at pH of 4 stimulated calcite cores better than HEDTA at 300°F at different injection rates. No face dissolution was observed at low injection rate and high temperature in the case of GLDA. The results obtained with carbonate cores saturated with nitrogen gas were almost similar to those obtained when the cores saturated with water. Using mutual solvent as a preflush in the oil-saturated cores removed most of the oil from the core and water-wet the calcite allowing reaction of chelants with calcite, similar to the case of water-saturated 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 | 0.001 |
| 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".