After a Decade of Microseismic Monitoring: Can We Evaluate Stimulation Effectiveness and Design Better Stimulations
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Over the past decade, microsesimic monitoring has become the approach most oftenused to gain an in-situ understanding of the rock’s response during hydraulic fracture stimulations. From initial monitoring performed in the Barnett Shale to monitoring currently being carried out for example in the Horn River and Marcellus formations, we review the evolution of microseismic monitoring from the viewpoint of data collection (single versus multi-well array configurations, utilization of long lateral stimulation wells), to data analysis, to the incorporation of microseismic parameters to constrain and validate reservoir models. Generally, we have observed that overall fracture height, width and length, orientation, and growth vary from formation to formation and within each formation, thereby highlighting the ongoing necessity for microseismic monitoring. Additionally, through the use of advanced microseismic analysis techniques, such as Seismic Moment Tensor Inversion (SMTI), details on rupture mechanisms have been used to assess stimulation effectiveness, define complex Discrete Fracture Networks (DFN) and provide estimates of Enhanced Fluid Flow (EFF), which assist in calibrating and validating reservoir models. Utilizing spatial and temporal distributions in DFN and EFF, along with estimates of fracture interconnectivity and complexity, the role of pre-existing fractures and fault structures in the rock matrix can be established and used to provide more realistic estimates of stimulation parameters such as Stimulated Reservoir Volume (SRV).
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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 | 0.001 | 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 | 0.000 | 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 it