Characterization of Microscopic Residual Oil Distribution during Waterflooding Using In Situ Computed Tomography
Bibliographic record
Abstract
In this study, a comprehensive and pragmatic experimental framework has been developed to quantitatively characterize the occurrence and categorization of microscopic residual oil distribution via in situ computed tomography (CT) techniques. More specifically, waterflooding displacement experiments were conducted and monitored through online CT scanning. In addition to incorporating the oil–water distribution within pores and throats, a three-dimensional (3D) digital core was subsequently reconstructed through threshold segmentation, facilitating the identification and reclassification of microscopic residual oil distribution based on its formation mechanisms and occurrence state. Finally, 3D digital core modeling was performed to determine the microscopic residual oil saturation at various occurrence states, and the microscopic residual oil saturation in pores and throats with different radii was determined by the use of the pore space intersection algorithm. For the microscopic residual oil after being displaced during waterflooding, continuous oil distribution of its original network has gradually evolved into a discontinuous one with multiple states and small discrete volumes. According to its formation mechanisms, microscopic residual oil can be divided into three types: Nondisplaced residual oil, throat-plugging residual oil, and adsorbed residual oil. Nondisplaced residual oil includes cluster-shaped residual oil and throat-shaped residual oil with a saturation of 16.76 and 2.70%, respectively. Throat-plugging residual oil includes droplet-shaped residual oil with a saturation of 9.23%, while adsorbed residual oil includes corner-shaped residual oil and film-shaped residual oil with an absolute content of 2.00 and 1.45%, respectively. When waterflooding reaches the high water-cut period, the microscopic residual oil primarily accumulates in the intermediate and large pores controlled by small throats.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".