Underlying Mechanisms of Tight Reservoir Wettability and Its Alteration
Bibliographic record
Abstract
Abstract The wettability of tight reservoir rock plays a critical role in affecting relative permeability and in turn oil recovery. However, the link between wettability and its effects on oil recovery remains poorly understood, and the potential to boost oil recovery by varying the wettability has not been fully explored. This work was an attempt to conduct a systematic experimental study to improve our understanding of wettability of tight oil reservoirs and the mechanisms of its alteration on oil recovery improvement. Contact angles of individual rock-forming minerals and reservoir rock samples were first measured in brines with different salinities. Then the minerals were aged separately with a medium crude oil with sufficient polar components to investigate their tendency for wettability alteration. As well, oil and water distributions inside tight core samples were scanned by a synchrotron-based computed tomography scanner. Contact angle measurements for all minerals and reservoir rocks showed initial water-wetting behavior. After aging with crude oil for over two months, polar components from the oil adsorbed onto the solid surfaces to alter their wettability to less water wet. Consequently, this wettability alteration contributed to oil and water redistribution and saturation change in reservoir cores. The experimental findings suggested that the wettability in tight reservoirs is a strong function of rock mineralogy, formation fluid properties, and saturation history. Preliminary numerical simulation revealed how rock wettability alteration could contribute to improved oil recovery through waterflooding.
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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 | 0.000 | 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 | 0.000 |
| 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".