Performance evaluation of <scp>SDAGM</scp> ‐coated microproppants in hydraulic fracturing using the lattice <scp>Boltzmann</scp> method
Bibliographic record
Abstract
Abstract Hydraulic fracturing has become a standard stimulation technology to enhance hydrocarbon production in unconventional reservoirs in recent decades. During organic‐rich tight carbonate reservoir stimulation, extensive microfractures will be created and pre‐existing microfractures will be opened in the far‐field during hydraulic fracturing, but they tend to close after the release of hydraulic pressure due to the larger sizes of the conventional proppants not fitting into the microfractures. The well productivity will be further enhanced if these microfractures can be held open during production, like the primary hydraulic fractures supported by proppants. Based on this idea, industry has introduced microproppants into the pad or pre‐pad fluid so that they can be placed into opened microfractures during hydraulic fracturing. In this work, we propose further enhancing the stimulation efficiency by introducing solid delayed acid generating materials (SDAGM)‐coated microproppants into the stimulation for the dual function of keeping microfractures open, and, through subsequent reactions of the coating materials with the carbonate formation, creating extra void space inside the microfractures. To prove this concept and help select the appropriate microproppant coating plan, lattice Boltzmann simulation is performed to measure the hydraulic conductivity of the stimulated microfractures under different scenarios that represent corresponding stimulation treatment schemes. The simulations showed that fracture conductivity of the microfractures can be significantly improved by placing SDAGM‐coated microproppant into them. Using the mixed uncoated and SDAGM‐coated microproppants (Scheme II) may have better fracture conductivity improvement than using the coated microproppant alone (Scheme I).
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".