Effect of Electrostatic Interactions on Water Uptake of Gas Shales: The Interplay of Solution Ionic Strength, Electrostatic Double Layer, and Shale Zeta Potential
Bibliographic record
Abstract
Abstract The uptake of water by rock matrix in a hydrocarbon producing well brings in both economic justification and environmental concerns. A detailed understanding of the water-rock interactions is essential for design and implication of hydrocarbon recovery techniques and environmental impact analysis. In this study, the water imbibition results are described by the electrostatic interactions between the water and shale samples. The effect of leachable ions in the shale, imbibed fluid ionic strength, electrostatic double layer, and zeta potential of shale are studied. The shale powders are washed with DI (deionized water) sequentially. Individual ion concentrations are obtained by ICP-MS analysis. The ionic strength and Debye length of the brine obtained from each step of the washing experiment are calculated. Zeta potential of the fresh and washed powders are measured in DI water and 10-times concentrated brines from first washing stage (CN brines). A set-up is designed to perform the imbibition experiments on the intact and washed shale powders. DI water and CN brines are used as the imbibing fluids. Washing with DI water leached ions out of the shale powders. After a maximum of 7 washing steps, the ionic strength of the resulting brine solution reached to a constant value which cannot be further reduced by washing. Zeta potential of shale powders in CN brines are substantially lower than the zeta potential of shale powders in DI water. This reduction in the zeta potential value to higher ionic strength of CN brines as compared with DI water. Imbibition experiments reveal that the CN brine solutions imbibe slower into the shale powders as compared with DI water. DI water imbibes faster in washed powders as compared with fresh powders. Debye length is correlated with imbibition rate, as higher Debye length of the solution results in faster imbibition. A reduction in the solution ionic strength increases the thickness of electrostatic double layer and zeta potential value. The thickness of electrostatic double layer in contact with the shale surface, which is modulated by the ionic strength of the in-situ brine solution, is an important factor that influence the particle zeta potential of shale as well as the imbibition rate of aqueous solution into shales.
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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".