Natural Fracture Characterization From Microseismic Source Mechanisms: A Comparison With FMI Data
Bibliographic record
Abstract
Abstract Microseismic monitoring of hydraulic fracture stimulation is used to map the extent of fracture growth during the completion of unconventional resource wells. Usually the geometry of the event distributions is used to infer fracture plane orientations, for instance when microseismic events are concentrated along a particular azimuth. Often the induced microseismicity is the result of reactivation of existing fractures in the reservoir. Source mechanism analysis that allows identification of the specific fracturing behavior of individual microseismic events can aid differentiation between reactivation of existing fractures and the creation of new fractures. This paper presents the results from the microseismic monitoring of a Mid-Continent horizontal gas shale well where failure planes of source mechanisms from the microseismic events are compared with fractures identified in a resistivity image log. The source mechanisms originate from failure on existing fracture planes, many of which the image log show to be partially or completely healed. The reactivation of these fracture planes are the dominant failure mechanism detected by the monitoring, but additional fracture planes were also likely stimulated by the treatment but seismicity associated with other fractures has a signal to noise ratio below that required to invert for source mechanisms. Enhanced production resulting from the stimulation is expected to result from a combination of fractures in the natural fracture network; those related to the source mechanisms and other fractures that may be opened aseismically. The result is a well-connected fracture network because of contributions of flow from multiple fracture orientations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".