After a Decade of Microseismic Monitoring: Can We Evaluate Stimulation Effectiveness and Design Better Stimulations
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).
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 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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".