Analysis of Surface and Downhole Microseismic Monitoring Coupled with Hydraulic Fracture Modeling in the Woodford Shale
Bibliographic record
Abstract
Abstract The work presented in this paper analyzes surface and downhole microseismic data for a horizontal well in the Woodford Shale in Oklahoma and compares those results with calibrated hydraulic fracture modeling. Hydraulic fracture models were created for each of five stages with a three-dimensional modeling software, incorporating available petrophysical data in order to match the recorded treatment pressure and the fracture geometry obtained from the microseismic data. Further analysis investigated the congruency of the downhole and the surface microseismic data, what differences they produced in a match if used exclusively, the influence of the number of events on the fracture geometry obtained from the microseismic data, the error of event location, the degree of complexity of the created fracture network, and the relationship between the magnitude of events and the time and location of their occurrence. The fracture models produced good matches for both pressure and fracture geometry but showed problems matching the fracture height due to cross-stage fracturing into parts of the reservoir that were already stimulated in a previous stage. Surface and downhole microseismic data overlapped in certain regions and picked up on different occurrences in others, giving a more complete picture of microseismic activity and fracture growth if used together. However, they deviated in terms of vertical event location with surface data showing more upward growth and downhole data showing more downward growth. In general, the downhole microseismic data showed that the stimulation treatment was successful in creating a fairly complex hydraulic fracture network for all stages, with microseismic recordings making flow paths visible governed by both paleo and present day stresses. Plots showing the speed of event generation, the cumulative seismic moment, and the event magnitude versus the event-to-receiver-distance identified interaction with pre-existing fault structures during Stages III and V.
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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 | 0.001 | 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".