Characteristics of oil distributions in forced and spontaneous imbibition of tight oil reservoir
Bibliographic record
Abstract
Matrix imbibition , which includes spontaneous imbibition (SI) and forced imbibition (FI), is the main mechanism of water-based methods, and can play a significant role in unlocking tight oil potentials as a tremendous amount of oil remains in the matrix following primary production . Previously, SI and FI have been investigated separately in pore-scale studies for several years. However, it is difficult for the results to provide guidance for selecting water-based methods owing to the different core samples and pore classification criteria adopted. Therefore, an integrated study of SI and FI is conducted on tight cores in order to understand the characteristics of oil contributions from different pores. In this work, 68 tight cores from the Chang 8 formation, Ordos Basin (China) are investigated. Nine cores are used to test native wettabilities ; then, rate-controlled porosimetry is conducted on a typical tight core. Finally, nuclear magnetic resonance is implemented to determine the oil distributions before and after SI and FI for six cores. Based on the petrophysical properties, the cores are classified into three permeability levels (0.06 mD, 0.1 mD, and 0.22 mD). The SI and FI results demonstrate that FI can always provide more than twice the oil recovery factor of SI in each permeability level. For FI, more than 40% of the produced oil is contributed by mesopores. With increasing permeability, macropores contribute more oil than micropores . For SI, the oil contribution from micropores can reach 53.34%. The permeability of 0.1 mD is a critical point at which the oil contribution of mesopores surpasses that of micropores.
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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 | 0.001 | 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 | 0.000 |
| 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".