Effect of Electrostatic Interactions on Water Uptake of Gas Shales: The Interplay of Solution Ionic Strength and Electrostatic Double Layer
Bibliographic record
Abstract
We conduct spontaneous imbibition experiments using different fluids (deionized, DI, water and brines) and different media (unwashed and washed shale powder) to study the wetting behavior of the shale samples from the Horn River Basin (HRB), a massive unconventional gas play in the Western Canadian Sedimentary Basin. As expected, unwashed shale powder imbibes DI water faster than brine. Surprisingly, washing the powders results in faster imbibition of DI water. The imbibition of DI water into washed shale powders, which have a reduced soluble/leachable ion content, cannot be fully explained by osmotic effects. We explain the observed imbibition profiles using the electrostatic interaction theory. We measure the ion concentration of the brines by ICP-MS analysis and determine the ionic strength, I, of the in situ formed brine. We also calculate the characteristic thickness of electrostatic double layer, κ –1, formed around the surface of charged shale powders. The results indicate that the imbibition rate depends on the κ –1 value of the in situ formed brine. Electrostatic interaction is part of the disjoining pressure which is not considered in the Young–Laplace equation. A higher κ –1 value enlarges the electrostatic interaction range, which results in formation of a thicker hydration shell around the surface of the shale powder and increases the imbibition rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".