What Can We Learn From the Child/Infill Well Fracturing in Shale Gas Reservoir? Some Interesting Insights
Bibliographic record
Abstract
Abstract Infill horizontal wells with fracturing treatment are being widely applied to enhance the recovery from shale gas reservoirs in Sichuan basin. Frac-hits are commonly treated as a hazard for the adjacent (infill) well production and wellbore. During the infill well fracturing of the gas shale in Sichuan basin, an interesting field phenomenon has been reported: (i) the number of microseismic (MS) events close to the boundary of the parent well SRV decreases sharply. It seems that there is a barrier or a local attenuation process that inhibits significant MS activity from developing within the parent well SRV. (ii) After infill well fracturing, a transitory pressure and production regain were detected in the adjacent parent well. In this work, an integrated flow-geomechanics coupling model has been built, which includes the parent well fracturing, parent well production, infill well fracturing and production. Involving with the field researches in North America, two cases in Fuling gas shale are discussed. The first case is based on an infill well in one main productive layer, while the second case is based on a multi-layer infill wells zone. Mechanism of the field phenomenon from Fuling gas shale infill wells are investigated by the integrated flow-geomechanics coupling model and evidenced by field data. Finally, several inspirations were proposed: (1) Only in gas shale (ultra-low permeable and naturally fractured reservoir)? (2) More geomechanical mechanisms for the MSEB effect? (3) Frac-hit is good or bad? How can we utilize it? More investigations are expected from scholars in different researching fields. Background The well production and pressure of shale gas reservoirs in Sichuan basin (Fuling, Changning, etc) drops over 90% and 75% in two years. Infill horizontal wells with fracturing treatment are being widely applied to enhance recovery from this reservoir.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".