Influencing Fracture Growth With Stage Sequencing
Bibliographic record
Abstract
Abstract This paper discusses a well-to-well spacing test on a 5 well pad in the Utica Shale that was stimulated with a unique stage sequencing plan. The stage sequencing plan provides an improved understanding of the way that fracture growth can be influenced by subsurface pressure differentials created by newly fractured, shut-in and depleted wells. Chemical (water) tracers and oil tracers were pumped into the center well of the 5 well pad. The non-traced wells on the pad, along with 2 wells on an adjacent pad, were all sampled during flowback and production. The samples were analyzed for the presence of oil and chemical tracers to determine the extent and degree of well-to-well communication. Additionally, surface microseismic data was collected and used to further assist in the study of the fracture growth. The tracer communication and microseismic data were represented together in a 3D visualization of the well pads. Generally, the tracers and microseismic events show that there is more extensive fracture growth from a treatment well to offsetting wells when there is no hydraulic pressure barrier between them. Better fracture containment and symmetry was observed on stages that were bounded on both sides by wells that were just fractured. Data from the study show that proper sequencing of the completions can mitigate the tendency for fractures to preferentially grow towards depleted wells. The study will therefore illustrate the value of tracer and microseismic data for understanding additional or new knowledge about multi-well pad stage sequencing and its role in fracture growth, and overall future well planning strategy.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".