Synchronous vertical fracture propagation of multi-layer radial wells for enhancing stimulated height in shale oil reservoir
Bibliographic record
Abstract
The diversity of interlayers in shale oil reservoir leads to a low degree of vertical reconstruction. This paper aims to propose a method to guide the synchronous initiation of hydraulic fractures in different layers by drilling multi-layer radial wells in spatial positions, and to form a fracture network that satisfies the vertical propagation range and complexity. In this paper, a 3D (three-dimensional) multi-layer radial well fracturing model considering fluid-mechanics coupling is established and the properties of shale oil reservoir are characterized according to the field geological profile. The influences of radial well spacing, fracturing fluid injection rate, and fracturing fluid viscosity on vertical fracture communication in multi-layer radial wells are investigated. The results show that the radial well has the characteristics of guiding fracture penetrating interlayers. Reducing radial well spacing and appropriately increasing injection rate and viscosity are beneficial to improving vertical fracture propagation ability. However, high fracture fluid viscosity under the same displacement will lead to a significant increase in fracture aperture and weaken the total fracture area. In addition, if the stress interference around the radial wells is low, the radial well can be located in the middle of each layer to minimize the fracture height limitation. This study can provide a solution idea for vertical propagation limitation of hydraulic fractures in shale oil 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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".