Refining Discrete Fracture Networks With Surface Microseismic Mechanism Inversion and Mechanism-Driven Event Location
Bibliographic record
Abstract
Abstract Microseismic event analysis is a valuable source of information that can play a pivotal role in optimizing well completion and spacing. This analysis can be taken a step further with the generation of discrete fracture networks (DFNs) from microseismic events. While DFNs can be modeled with microseismic event locations only, source mechanisms inverted from near surface-acquired microseismic data provide greater constraints for the DFN model so that the orientation of failure planes responsible for events can be explicitly assigned. The differences between such DFN realizations based on event locations only and source-mechanism constrained DFN realizations are evident in areas with significant geological complexity. Three iterations of a DFN model were produced from a microsesimic monitoring project in the Barnett shale. The fracture network of the first iteration is modeled stochastically using only basic geologic assumptions for the area and microseismic event locations and the orientations of trends formed by the events. The second iteration is refined by deterministically locating fractures in the model and defining the fracture orientations using a source mechanism determined from the microseismic point set. The third iteration uses the results from a mechanism scan on an event per event basis to determine the best source mechanism that fits the polarity reversal signature observed on the surface array. Refining the model by determining the mechanism of individual events can identify multiple fracture orientations within the point set. In this data set two distinct mechanisms were identified, further analysis of which identified separate event energy distributions for the two mechanisms. The changes in the model can be quantitatively evaluated with analysis of flow properties generated from the DFN and output to the stimulated reservoir volume (SRV). While changes in the SRV and total fracture volume for models presented in this study are most significant between the first two iterations, the total permeability change across the geocellular volume is significant between all three iterations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".