In Situ Sequestration of a Hydraulic Fracturing Fluid in Longmaxi Shale Gas Formation in the Sichuan Basin
Bibliographic record
Abstract
Due to high salinity and complex components including varied inorganic salts, organic agents, released heavy metals, radioactive elements and clastic particles, environmental and economical treatments of a huge amount of flowback wastewater from shale gas wells have attracted both public concerns and industrial interest. Unlike the existing methods such as deep well reinjection and recycling, this paper explores the feasibility of directly reserving an injected hydraulic fracturing (HF) fluid in a shale formation utilizing its strong capillary imbibition and permanent sequestration capacity. Fluid imbibition-flowback and nuclear magnetic resonance experiments are conducted to check the microscopic reliability: once being imbibed into shale, only less than 20% of the total fluid is driven out even under a pressure gradient of 22.1–62.6 MPa/m. Macroscopic flowback data from Longmaxi shale gas wells in the Sichuan Basin further demonstrates that by adding 10–30 days to current shut-in operations the flowback rates of a HF fluid decrease approximately 5–15%, meaning a reduction of 2000–6000 m 3 wastewater per single well without extra technical procedures or additives. Moreover, fractured sample displacement experiments, simulations, and field data statistics confirm that extending shut-in time causes an overall improvement in well productivity and flowback fluid quality.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".