Hydraulic Fracturing Modeling, Fracture Network, and Microseismic Monitoring
Bibliographic record
Abstract
Abstract This paper introduces a 3D hydraulic fracturing propagation model (3D-HFPM) for evaluating fracture extension, geometry, stress response, fracture spacing, and potential for refracturing. The model is illustrated with data of the Horn River shale of Canada. The model is developed using a combination of finite element method (FEM) and boundary element method (BEM) for evaluating fluid-flow, fracture deformation, and stress change in the reservoir. The model is calibrated using a limited amount of microseismic observations and recreate the fracture network when microseismic data are unavailable. An adapting meshing algorithm is incorporated to improve the capacity of the model to handle large and complex fracture networks such as the ones found in low permeability reservoirs. The continuity of fracture propagation and fluid leak-off during stimulation may be high enough to connect different production intervals and to create interference between stages, especially in wells with small path fracture spacing and multi-level completions. The comparison between the propagation model and microseismic data shows good agreement as the number of events increases as the fracture propagates into the reservoir. However, using only microseismic data to calculate the extension of the hydraulic fracture results in an overestimation of the fracture length. The model quantifies the altered stress zone, which is helpful to determine possible fracture reorientation and spacing. The evaluation of stress shadow and fracture reorientation reveals the advantages of refracturing using new over old perforations. The operation restores fracture conductivity and increases the fracture network as well as the drainage areas leading to an economic operation. The model improves the characterization of the Stimulated Reservoir Volume (SRV) in tight and shale reservoirs in those cases where microseismic data are scarce. Furthermore, the model is a viable tool for evaluating potential refracturing candidates.
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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".