An integrated approach to characterize hydraulic fracturing-induced seismicity in shale reservoirs
Bibliographic record
Abstract
In this study, an integrated approach of geology, geophysics, geomechanics and hydrodynamics is developed to characterize the hydraulic fracturing-induced seismicity in unconventional shale reservoirs. Firstly, a structural model including the faults and surfaces is developed by the multi-component 3D seismic interpretation. The local structure attributes analysis, and ant-tracking technique are then applied to identify the pre-existing faults and fractures distribution, where their distributions are calibrated by focal mechanism inversion of the mainshock events. Subsequently, a 3D geomechanical model is built, which incorporates the rock mechanics and in-situ stress regime into the structure model. Additionally, the hydraulic fracturing processes are simulated and hydraulic fractures geometry and fluid pressure distribution within the hydraulic fractures are estimated by history matching the net pressure. Finally, the fluid flow in hydraulic fractures is coupled with the geomechanical model to characterize the pore pressure diffusion and poroelastic stress perturbation that causes the fault to slip. As the field cases, the Mw 3.6 and Mw 4.1 induced seismicity near the Crooked Lake region are investigated to evaluate the applicability of the integrated approach. Moreover, the Mw 3.2 case and Mw 4.18 cases are analyzed to explore the controlling factors of hydraulic fracturing-induced seismicity in Western Canada. Based on eight field cases in Fox Creek, the susceptibility of hydraulic fracturing-induced seismicity towards fracturing stimulations are evaluated and potential mitigation strategies are proposed to reduce future seismicity risks. Finally, a comprehensive machine-learning approach is proposed to evaluate the susceptibility and mitigate the risks of hydraulically induced seismicity, as well as forecast the shale gas production via the integration of geological, geomechanical and operational factors in Fox Creek.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 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".