Engineering Hydraulic Treatment of Existing Fracture Networks
Bibliographic record
Abstract
Abstract Observations from field monitoring indicate the presence of existing fracture networks significantly affect, and may control, the history of hydraulic treatments. Existing fractures allow pathways for treatment fluids to migrate efficiently through the formation, stimulating connectivity to larger reservoir volumes. This paper examines the relationship between hydraulic treatments and fracture networks, and investigates the ability to engineer the hydraulic conductivity of the stimulated volume. Fracture network engineering (FNE) involves the engineering design of rock mass disturbance through the use of advanced techniques to model fractured rock masses numerically, and then correlate field observations with simulated fractures generated within the models. Modeling algorithms accurately address the hydromechanical physics of hydraulic treatments developed across a range of applications in rock engineering. A Synthetic Rock Mass (SRM) model is constructed by explicitly defining a discrete fracture network within a modeled rock matrix. Hydraulic treatment into the SRM allows fracture dilatancy, propagation and shearing to be realized in the emergent behavior of the bonded particle model in three dimensions and at the scale of the treated volume. Simulated microseismicity is generated when the fractures are disturbed or propagated providing a method for correlation with field observations. Illustrative models show how hydraulic treatments stimulate fracture networks and generate new hydraulic fractures in volumes not expected in conventional design analysis connecting existing fractures with preferential alignment. By combining SRM models with field observations it is possible to investigate the relation and sensitivity of hydraulic treatments to existing fracture networks, and therefore, engineer the most optimal use of these fractures in the performance of a given project. Developmental challenges are discussed, including the sensitivity of the techniques to fracture network uncertainties and the accurate representation of microseismic source data within the models.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".