Pulse injection to suppress invasion front fingering: a clean technology to improve oil recovery from oilfields
Bibliographic record
Abstract
During oil and gas field development, the fingering of injected fluid in porous media can significantly reduce its swept volume and oil recovery. Pulse injection is a clean production technology for enhancing oil recovery (EOR). However, there are still serious deficiencies in the research on the mechanism of expanding the swept volume and parameter optimization. In this paper, systematic experiments are conducted using a sizable etching micromodel (5 cm × 3 cm) to investigate the fingering suppression effect and mechanisms of pulse injection. A method for classifying invasion patterns is established based on the morphology and fractal dimension. The single pore-throat force analysis identified the fundamental reason for fingering as the uneven force distribution in different invasion directions of the next pore-throat. The multiple pore-throat invasion characteristic revealed that fingering is primarily presented by layer flow on the wall surface during the imbibition process, whereas the burst mode is observed during the drainage process. Pulse injection can transform viscous fingering into capillary fingering and significantly expand the swept volume within a certain parameter range (amplitude (A) × frequency (F) > 0.25, A is greater than 1.0, and F is about 0.75 for better effects). Pulse injection can generate an instantaneous pressure gradient, promote the invasion front to invade the bilateral pore throats, and expand the swept volume. Pulse injection can transform large cluster remaining oil into cluster (with small volumes), slit, and film remaining oil. The cumulative effect of front deformation at the pore-throat determines the further increase in swept volume caused by the increase in amplitude and frequency. The research content of this paper will provide theoretical and applied research on pulse injection as a clean EOR technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".