Using Microseismic Events to Constrain Fracture Network Models and Implications for Generating Fracture Flow Properties for Reservoir Simulation
Bibliographic record
Abstract
Abstract Microseismic monitoring of hydraulic fracture stimulation treatments has done much to diminish the expectation of engineers and geoscientists that symmetrical bi-wing fractures extending away from the well bore from as a result of the treatment. Mapping of microseismic event locations indicates that more often, zones of high complexity form which suggest multiple rock failure mechanisms could be in play during the stimulation treatment. The complexity of the failure is further complicated, or perhaps explained, by the interaction of the perturbed stresses with existing fractures in the reservoir relative to the unperturbed stress state of the reservoir. Existing fracture planes favorably oriented for shear will fail at lower stresses than are required to create new fractures. Geologic mapping and regional to local in-situ stress information will allow informed interpretation of the resulting microseismicity patterns as well as providing predictive capability for fracturing patterns of treatments in subsequent area wells and production planning. Correlative to the improved fracture mapping is the use of the fracture interpretation as input to fractured reservoir modeling and fractured reservoir simulation. Utilizing microseismicity data not only to constrain location of fractures, but also fracture size, shape and orientation allows creation of improved fractured reservoir models based on geologic concepts and supported by the real time data. In this paper two examples are presented from a hydraulic stimulation of North American mid-continent wells that were monitored with a surface-based geophone array. The resulting microseismicity patterns in both wells show that the fracture development was strongly influenced by pre-existing discontinuities (fractures or faults), which are easily explained by geologic and in-situ stress analysis. The fracture interpretation and microseismicity data from one example is used to generate a discrete fracture network from which fracture flow properties are created in a geocellular model. The resulting model provides a quantitative framework for production history mapping and reservoir behavior, with hard constraints for the behavior of the dominant fractures in the fracture network.
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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 | 0.001 | 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 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".