Integrated study of hydraulic/CO2 fracturing and production coupled with a THM-D process in ultra-shallow shale reservoirs
Bibliographic record
Abstract
To explore fracturing technology for vertical wells in ultra-shallow shale gas reservoirs, a coupled thermo-hydro-mechanical-damage (THM-D) fracturing and production integration model is established in this study. In addition, a new coupled hydro-mechanical damage model is established to calculate fracture evolution. These two models are validated through theoretical models and field data, respectively. Based on these models, the quality of fracturing under different geological parameters, fracturing parameters, and fracturing technology is compared and analyzed. The results show that the distribution of natural fractures significantly influences fracturing and production. In addition, due to the high leak-off in the ultra-shallow shale reservoir, the total fracture length and cumulative production after 720 days of carbon dioxide fracturing are only 70.35% and 77.26% of the values achieved by hydraulic fracturing, respectively. Therefore, it is necessary to consider reducing carbon dioxide leak-off in the design of carbon dioxide fracturing in ultra-shallow shale reservoirs. Fracturing efficiency also should be considered when designing fracturing time. When the injection rate is 5 m 3 /min, the efficiency drops sharply if the fracturing time exceeds 67.45 min. The production of hydraulic fracturing and carbon dioxide fractured wells has also been studied when fracturing methods without proppant are used. This study found that a satisfactory production rate can also be achieved in ultra-shallow shale gas reservoirs when fracturing without proppant.
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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) | 0.000 | 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".