Identification of Activated Fracture Networks Using Microseismic Spatial Anomalies, b-values, and Magnitude Analyses in Horn River Basin
Bibliographic record
Abstract
Abstract We analyzed microseismic spatial and temporal distribution, magnitudes, b-values and treatment data to interpret and explain the observed anomalies in microseismic events recorded during exploitation of Shale Gas reservoirs in the Horn River Basin of Canada. We estimated the directional diffusivity to define the microseismicity front curve for each stage of hydraulic fracturing. Based on our definition of front curves, we managed to separate most of the microseismic events data that are related to natural fracture activation from hydraulic fracturing events. We analyzed the b-values for microseismic events of each stage before and after separating fracture activation microseismic events from original data and created a map of b-values in the study area. This allowed us to locate activated fractures mostly in the northeastern part of the study well pad. The b- value map agrees with our assumption of activated fracture locations and high ratio of seismic activities. Suggested fracture locations agree with anomalous events' density, energy distribution and treatment data. We are defining and proposing intermediate b-values for calculation of the stimulated reservoir volume (SRV) in areas with both hydraulically fractured events and events related to natural fracture network activation in those instances where the separation of events based on their origin is not viable.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".