Effects of Chemical Additives on Dynamic Capillary Pressure during Waterflooding in Low Permeability Reservoirs
Bibliographic record
Abstract
It is suggested that the capillary pressure–fluid saturation relationship should be determined as a function of a dynamic coefficient (τ) and the time derivative of fluid saturation (∂ S w /∂ t ), indicating a dynamic capillary pressure in most cases, which will increase the flowing resistance and injection pressure for oil-wet reservoirs, especially in low permeability formations. To decrease the injection pressure and improve injection, various chemical additives such as surfactants and fluorides have been widely used in the waterflooding process in low permeability reservoirs. Effects and mechanisms of these chemical additives are yet not well-known. In this paper, a series of specially designed waterflooding experiments were conducted to investigate the effects and mechanisms of surfactant additives on the dynamic capillary pressure–fluid saturation relationship in low permeability reservoirs. In the experiment, capillary pressure–fluid saturation relationships in three low permeability core samples were examined during the waterflooding process, as well as the surfactant added waterflooding process. Then, local dynamic coefficients in the core samples were calculated and compared. Results indicate that low permeability reservoirs present a high dynamic coefficient, therefore generating high dynamic capillary pressure, which is the cause of high injection pressure during waterflooding. Furthermore, surfactant additives can reduce the dynamic coefficient and capillary pressure significantly, and the lower permeability core sample shows higher dynamic capillary pressure reduction, indicating that surfactant added waterflooding can significantly reduce injection pressure in low permeability reservoirs. This work provides a method to investigate the interaction among fluids and porous media during waterflooding through the examination of dynamic capillarity.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".