Estimation of Fracture Characteristic within Stimulated Rock Volume using Finite Element and Semi-Analytical Approaches
Bibliographic record
Abstract
Abstract Hydraulic fracturing increases the drainage area and effective permeability of unconventional oil and gas reservoirs by creating a fracture network or stimulated rock volume (SRV) within the reservoir rock. The dimensions of the SRV and its permeability are the key parameters that enhance the unconventional reservoirs' performance after the hydraulic fracture operation. Simulation of the SRV to obtain its dimension and permeability can be used to determine the optimum hydraulic fracture treatment parameters and production. In this study, finite element analysis is employed to determine the SRV characteristics based on field data from a hydraulic fracturing job in a horizontal well penetrating the Glauconite formation in Hoadley field, Alberta, Canada. The dimensions of the SRV are calibrated from the microseismic data. The fracture propagation pressure of the finite element model is matched to the field value by altering the permeability of the SRV. The matched model is used to obtain the in-situ stress changes and the pressure drop within the SRV. The SRV permeability and the pressure drop are used to calculate the aperture, the number, and the spacing of the fractures within the SRV using a semi-analytical approach. The final outputs can be used to optimize the future hydraulic fracture design at the Hoadley field or at other fields that have similar geomechanical properties. It could also be used to predict the reservoir production after the hydraulic fracturing and to provide estimates of changes in the in-situ stresses around the stimulated horizontal wellbore.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".