Techniques to estimate fracture effectiveness when mapping low‐magnitude microseismicity
Bibliographic record
Abstract
Hydraulic fracture mapping by locating microseismic events related to rock fracturing is used to evaluate the effectiveness of the stimulation in low-permeability reservoirs. The geometry of the events is used to infer fracture orientation, particularly in the case where events line up along an azimuth, or have a planar distribution in 3 dimensions. When the induced the microseismic eventshave a low signal-to-noise ratio (either due to low magnitude or propagation effects) their locations can have a high degree of uncertainty. Low signal-to-noise events are not as accurately located in the reservoir, or are not detected at all, so that the extent of fracture stimulated reservoir may be underestimated. In the Bakken Formation of the Williston Basin, we combine geological analysis with process-based and stochastic fracture modeling to build multiple possible fracture model realizations. Specific parameters in the models can be modified while honoring a realistic range for each parameter in order to explore the range of uncertainty. Fracture flow properties generated from the fracture models are validated via history matching iterations. The validated fracture models, in turn, provide a means to calculate a geometrically-constrained volume of rock and fracture permeability that can be used for estimating production. This paper presents a methodology for deciding which fracture parameters to vary (the high sensitivity parameters) in order to minimize the number of realizations that need to be generated during the model conditioning phase of a reservoir simulation program.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".