Storage v. production: challenges for reservoir modelling and simulation practitioners
Bibliographic record
Abstract
The rising interest in subsurface CO 2 storage makes new calls on reservoir modelling skills, most of which have been developed for hydrocarbon production scenarios. The question for practitioners is: to what extent can the familiar production tools be transferred to the world of storage? In this paper, areas requiring attention are highlighted and high-resolution models are used to compare the behaviour of simulators for production v. storage for two reservoir analogue examples. It is concluded that modelling for storage makes a significant call on multi-scale modelling, to a much greater extent than in production scenarios, and the simplification or omission of reservoir heterogeneities (sometimes tolerable in production scenarios) are much less tolerable when modelling storage. Key static model heterogeneities include the modelling of faults as 3D features, the inclusion of fine-scale reservoir permeability contrasts and the avoidance of net reservoir cut-offs. For dynamic models, use of equation of state is necessary for storage in depleted fields, and correct representation of hysteretic effects of plume migration are a requirement for modelling in aquifers (always) and depleted fields (usually). Modelling for storage, especially for saline aquifers, sets the challenge of modelling volumes previously considered to be at exploration scale, but with an effective resolution more typical of production scales.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".