An Interpretation of Microseismic Spatial Anomalies,<i>b</i>-values, and Magnitude Analyses To Identify Activated Fracture Networks in Horn River Basin
Bibliographic record
Abstract
Summary 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. The b-value shows the relationship between the number of seismic events in a certain area and their magnitudes in a semilogarithmic scale. The b-value is important because small changes in b-value represent large changes in the predicted number of seismic events. In this study, b-value is considered as an indicator of the mechanism of observed microseismicity during hydraulic-fracturing treatments. We estimated the directional diffusivity to define the microseismicity front curve for each stage of hydraulic fracturing. On the basis of our definition of an average front curve, we managed to separate most of the microseismic events that are related to natural-fracture activation from hydraulic-fracturing microseismic events. We analyzed 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 approximately locate activated fractures mostly in the northeastern part of the study wellpad. The b-value map agrees with our assumption of activated-fracture locations and high ratio of seismic activities. The dominant direction of the suggested activated natural fractures agrees with the general trend of the Trout Lake fault zone located approximately 20 km west of the study area. Suggested fracture direction also agrees with anomalous-events density, energy distribution, and treatment data. We are 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 in which it is not viable to separate events based on their origin.
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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 | 0.002 | 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) | 0.001 | 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".