Quantifying Hydraulic Fracture Geometry and Morphology in a Multi-Cluster, Multi-Stage, Hydraulically Fractured Well Using Volume-to-First-Response Analysis
Bibliographic record
Abstract
Abstract This study presents a novel analytical framework aimed at replicating hydraulic fracturing geometry at the cluster level within a multi-cluster, multi-stage, hydraulically fractured well. The fundamental Sneddon's equation is utilized to develop a mathematical solution that quantifies poroelastic stress shadow effects, taking into account both inter and intra-stage and intra-stage stress shadowing effects. The proposed approach integrates the fundamental solution of a blade-like fracture in the Perkins-Kern-Nordgren (PKN) model to determine fracture geometry, considering fluid partitioning between clusters resulting from stress shadowing and cluster competition. The Volume-to-First-Response (VFR) parameter obtained from fracturing diagnostics, such as Fiber Optics or Sealed Wellbore Pressure Monitoring serves as the basis for replicating the fracture geometry at the cluster level. The application of the proposed methodology is demonstrated using an example of a horizontal well that is monitored by Sealed Wellbore Pressure Monitoring in the Montney Formation, located in the region of the Northeast British Columbia, Canada. The proposed method offers a rapid and efficient approach to utilize the VFR parameter for replicating hydraulic fracturing geometry on a cluster-by-cluster basis, capturing the fracture half-length and fracture height.
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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 | 0.001 | 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) | 0.001 | 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".