Modelling shale spontaneous water intake using semi‐analytical and numerical approaches
Bibliographic record
Abstract
Abstract Shale matrix water intake during hydraulic fracturing is considered undesirable due to the high volume of unrestrained water loss; however, it is also rewarding because it can reduce the possibility of groundwater contamination. The water intake also plays a dual role in changing the shale production performance; water imbibition into matrix pores can dramatically reduce the effective permeability and, at the same time, might enhance hydrocarbon production by creating adsorption‐induced micro‐fractures. Quantifying the formation intake capacity and its breadth is the key to optimizing fracturing operation, flowback, and production strategy. This work entails complementary semi‐analytical and numerical analyses and investigates the effects of basic rock and fluid properties, wettability heterogeneity, and pore space connectivity on the shale imbibition characteristics. We consider a semi‐analytical formulation of the spontaneous water imbibition, together with model results and validations, and try to provide a framework to link the capillary imbibition capacity/rate to lab‐scale observations. The core‐scale measurements provide input for a sensitivity study on the matrix imbibition capacity in six shale plays in North America. The results suggest that rock permeability, hydraulic tortuosity, and initial and residual hydrocarbon saturations are among the most influential factors on the spontaneous water intake during shut‐in periods. Direct quasi‐static simulations are then conducted through a submicron tomography image of the Eagle Ford shale, and two‐phase pore‐level fluid occupancies are reconstructed during spontaneous and forced imbibition processes. According to the numerical results, the presence of continuous organic matter laminae can lower the destructive effects of water imbibition on the hydrocarbon permeability.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".