Effect of Water Salinity on Shale Reservoir Productivity
Bibliographic record
Abstract
It is well known that rocks containing water-reactive clays may swell when contacting with fresh water. In a conventional formation, this swelling may cause wellbore stability problem or damage formation by reducing its permeability. However, the effect of water salinity on shale rocks may be different. This issue is investigated in this paper. Shale rocks were immersed in water of different salinities. Shale rocks used were Mancos, Marcellus, Barnett and Eagle Ford. Different concentrations of NaCl and KCl salt solutions from 0% to 30% by weight were added in the water. It was observed that Mancos core plugs were crushed into loose grains (fragmented) at low salinity solutions up to 15%. Barnett core plugs showed consecutive cracks along bedding planes at low salinities. Minor cracks were seen on Marcellus, while no cracks at all were found in Eagle Ford core plugs at low salinities. When the shale plugs were saturated with oil, 2-15% oil was recovered by water spontaneous imbibition, depending on water salinity and rock mineralogy. Similar observations were made when shale core plugs were applied to an overburden pressure. The results from this paper help us to understand the drive mechanisms in shale oil and shale gas reservoirs. It also stimulates us to explore new ways to improve oil and gas recovery in shale reservoirs. Key words : Water salinity; Shale reservoir; Oil and gas recovery
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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 | 0.000 |
| 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".