Imbibition Oil Recovery from Tight Rocks with Dual-Wettability Pore-Network A Montney Case Study
Bibliographic record
Abstract
Abstract Previous studies demonstrate that the Montney rock samples have a dual-wettability pore network. Recovery of the oil retained in the small hydrophobic pores is a unique challenge. In this study, we apply dual-core imbibition (DCI) method on several Montney core plugs and introduce imbibition-recovery (IR) trio to investigate the recovery mechanisms in rocks with dual-wettability pore network. First, we evaluate the wetting affinity of five twin core-plugs from the Montney Formation by measuring spontaneous imbibition of reservoir oil and brine, and by measuring equilibrium contact angle. We place one plug of each pair in the oil and the other in the brine, and measure the weight change periodically. Second, we place the oil-saturated samples in the brine to visualize the expelled oil droplets and measure volume of the recovered oil. We comparatively analyze the spontaneous imbibition data from the first step and the recovery data of the second step in one imbibition-recovery trio (oil imbibition, brine imbibition, and imbibition oil recovery). The results of air-liquid contact angle and spontaneous imbibition on dry samples suggest that the affinity of the samples to oil is higher than that to brine, in an air-liquid system. However, the results of liquid-liquid contact angle and counter-current imbibition tests suggest that the affinity of the samples to water is higher than that to oil, in a liquid-liquid system. For each twin set, the oil recovery curve follows the trend of brine imbibition curve, and the final oil recovery is always less than the equilibrated water uptake of dry samples. This observation indicates that water can only access the hydrophilic part of the pore network initially saturated with oil. Finally, we introduce a porosity-based model to analyze oil-recovery data.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".