Investigation of the influence of natural fractures and in situ stress on hydraulic fracture propagation using a distinct-element approach
Bibliographic record
Abstract
Hydraulic fracturing is the primary means for enhancing rock mass permeability and improving well productivity in tight reservoir rocks. Significant advances have been made in hydraulic fracturing theory and the development of design simulators; however, these generally rely on continuum treatments of the rock mass. In situ, the geological conditions are much more complex, complicated by the presence of natural fractures and planes of weakness such as bedding planes, joints, and faults. Further complexity arises from the influence of the in situ stress field, which has its own heterogeneity. Together, these factors may either enhance or diminish the effectiveness of the hydraulic fracturing treatment and subsequent hydrocarbon production. Results are presented here from a series of two-dimensional (2-D) numerical experiments investigating the influence of natural fractures on the modeling of hydraulic fracture propagation. Distinct-element techniques applying a transient, coupled hydromechanical solution are evaluated with respect to their ability to account for both tensile rupture of intact rock in response to fluid injection and shear and dilation along existing joints. A Voronoi tessellation scheme is used to add the necessary degrees of freedom to model the propagation path of a hydraulically driven fracture. The analysis is carried out for several geometrical variants related to hypothetical geological scenarios simulating a naturally fractured shale gas reservoir. The results show that key interactions develop with the natural fractures that influence the size, orientation, and path of the hydraulic fracture as well as the stimulated volume. These interactions may also decrease the size and effectiveness of the stimulation by diverting the injected fluid and proppant and by limiting the extent of the hydraulic fracture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".