Integrated Interpretation of Microseismic with Surface Seismic Data in a Tight Gas Reservoir, Central Alberta, Canada
Bibliographic record
Abstract
Integrated interpretation of microseismicity with surface seismic data can provide valuable information about reservoir characteristics, mechanical stratigraphy, induced and pre-existing fracture systems. Although there are numerous integrated studies that focus on unconventional plays, relatively little attention has been given to tight gas environments. Typical interpretation of microseismic data focuses on the spatial and temporal distribution of microseismic events to estimate stimulated reservoir volume and, in some cases, to infer the character and geometry of discrete fracture networks. This thesis describes a methodology for integrated interpretation of 3D seismic data with microseismicity recorded during the open hole stimulation of two horizontal treatment wells of a tight-sand unit deposited in the Hoadley field, a Cretaceous marine barrier-bar complex in Western Canada. I introduce a novel approach, Microseismic Facies Analysis (MFA), to extract additional information from microseismic clusters. The interpreted microseismic facies are then correlated with surface seismic attributes in order to delineate reservoir partitions that are interpreted to reflect lithofacies variations associated with depositional trends.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".