Correlation of Stimulated Rock Volume from Microseismic Pointsets to Production Data - A Horn River Case Study
Bibliographic record
Abstract
Abstract Hydraulic fracture monitoring from microseismic allows operators to optimize completions through a clear understanding and correlation of the reservoir response to stimulation. Furthermore it helps operators to improve production and avoid out of zone growth by identifying patterns of fluid movement, fracture growth and connectivity. These critical insights allow refinements to the treatment plan, and provide useful insights for long-term improvements regarding well spacing, well design, and completion design. Shale reservoirs with very low permeability in the nano darcy range require a large fracture network to increase well performance. In these reservoirs, unless natural pre-existing fractures and faults have been reactivated and hydraulically opened to create a complex and well-connected network, pore pressure changes do not permeate far from the fractures. As a result, the microseismic pointset roughly corresponds to the size of the real fracture network which offers a means to estimate the stimulated rock volume (SRV). Although the producing fracture network could be smaller than the total SRV by a substantial percentage, it is expected that the effective network and the total SRV show a positive correlation. However, SRV is not the only indicator of well productivity. In a given SRV, the quality of the reservoir and parameters such as fracture conductivity and fracture spacing will affect production and can have a major impact on recovery calculations. In this study, stimulated rock volumes obtained from microseismic pointsets are correlated with actual field production. The correlations are used to illustrate how this concept can optimize treatment design, well spacing, and stage spacing through correlation of the reservoir response to hydraulic fracturing and production data. The correlation between production and SRV for each well shows that larger SRVs result in higher well production regardless of the percentage of the SRV that contains proppant filled fractures. The direct relationship of the microseismic pointsets and production can be used to predict a new well's potential productivity immediately upon completion of the stimulation job. This suggests that a key completion strategy is to create a large and effective SRV to provide maximum recovery and well performance monitored microseismically to provide production prediction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".