Relationship between Microseismic Activity, Hydrofracture, and Stimulated Zone Growth Based on a Numerical Damage Model: An Example from Bossier Sands
Bibliographic record
Abstract
Abstract The key property controlling reservoir productivity is permeability. Permeability enhancement during hydraulic fracturing, to some degree, can be inferred from microseismic monitoring. In this study, we numerically model microseismicity occurring along the hydraulic fracture, compare it to the recorded microseismic activity and predict permeability of the reservoir based on the motion on the fractures. We present a fully coupled fluid-geomechanical model of hydraulic fracture propagation in the heterogeneous reservoir. The fluid part is solved using the commercial code Geosim and the geomechanical and microseismic solutions are constructed using the damage mechanics. Permeability enhancement is calculated from the fracture displacement. We distinguish three fracture scales in terms of model implementation: the main hydraulic fracture (~100 m scale), new fracturing on the scale of microseismic events (~ 2-10 m scale) and microfractures (under 2-10 m scale). We apply this technique to the hydrofracturing treatment in the Bossier sandstone where we focus on the effect of the preexisting fracture heterogeneities on the reservoir microseismic activity. We use the observed microseismicity as a guide to describe the reservoir heterogeneity. Even though we match the observed microseismicity very well in time and space, the permeability enhancement related to the formation of the new fractures is very scattered and the increased leak-off does not change in time enough to characterize the total leak-off into the formation. Permeability determined from the new fractures is several orders lower than permeability of the main hydrofracture. Such an enhancement is not efficient in this case and cannot drive enough fluid into the formation and therefore permeability of the main fracture remains the main driving mechanism. However, the effect on the production may be different and needs to be explored. The heterogeneity of the reservoir seems to be a very important property for the success of the hydraulic fracturing.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".