Reservoir Characterization and Coupled Reservoir-Geomechanical Simulation of CBM Using GSI - Case Studies
Bibliographic record
Abstract
Abstract A hydro-geomechanical coalbed methane reservoir characterization workflow is reviewed and applied to three field cases from the same coalseam formation. The workflow begins from core sample characterization and ends at reservoir performance. A core sample and geophysical logging rock mass characterization approach using the newly adopted Geological Strength Index (GSI) is used. GSI was developed for Civil Engineering tunneling projects to characterize fractured rock masses and is adopted here with new functions to related GSI to observed Young's modulus and then included for permeability changes during production. The GSI characterization is applied to three separate reservoir geomechanical simulation models with separate and distinct production profiles. Reservoir and geomechanical data to populate the models comes from three sources: Nexen, the University of Alberta, and the Alberta Energy Regulator. The significance of this study is to investigate the influence of geomechanics on well productivity for fractured reservoirs using a common geological parameter which relates fracture intensity to mechanical properties.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".