An Adaptive Continuum/Discontinuum Coupled Reservoir Geomechanics Simulation Approach for Fractured Reservoirs
Bibliographic record
Abstract
Abstract Geomechanical effects during several stages of the reservoir life cycle are becoming more important in understanding the overall behaviour of the reservoir. Fractured reservoirs are generally recognized to be sensitive to changes in effective stress caused by production activities including production and injection processes. Using a reservoir simulation approach which incorporates geomechanical processes occurring in a fractured reservoir offers insight into reservoir management strategies. Some of the processes include changes in effective stress due to pressure depletion and/or thermal recovery, creation of new fractures, and changes in fracture apertures. All process effects influence fracture and matrix permeability and porosity and, in some cases can be identified by changes in acoustic properties and seismicity. A reservoir simulation approach which attempts to capture all of these processes must link four simulation approaches in an adaptive nature. A reservoir simulator is used to represent the complex fluid flow processes found in thermal and multi component fluid reservoirs. A continuum geomechanical simulator is used to capture the effects of changes in reservoir pressure, temperature and fluid volumes on the effective stress field. A particle flow code with an embedded fracture network is applied to capture the changes in apertures, creation of new fractures, and dynamic geomechanical continuum properties as well as the acoustic response. A discrete fracture network program is used to create equivalent permeabilities and porosities, while accounting for changes in apertures and new formed fractures. All of these codes are coupled to capture the complex processes described above in adaptive reservoir continuum/discontinuum coupling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".