Optimising Gas Injection in Carbonate Reservoirs Using High-Resolution Outcrop Analogue Models
Bibliographic record
Abstract
Abstract Outcrop analogue studies for complex subsurface reservoirs have become increasingly common because they allow us to integrate reservoir characterisation with reservoir simulation. This enables us to correlate distinct geological features that can be observed in the outcrop but are normally upscaled in dynamic models, to complex flow phenomena present in real reservoirs. Hence we can construct static models that are better calibrated because they contain the key geological structures controlling the flow behaviour and translate them into dynamic models that are upscaled properly. In this study we use a high resolution simulation model of a middle Jurassic carbonate ramp outcrop from the High Atlas Mountains of Morocco, which can be regarded as an analogue for the Arab D formation, to investigate fluid flow processes during enhanced oil recovery (EOR). The outcrop analogue model contains a wide range of sedimentological and structural geological features, including patch reefs, mollusc banks, mud mounds and fractures. Our work aims to improve our understanding of the flow dynamics occurring during secondary and tertiary gas injection in complex carbonate reservoirs. We simulated gas injection as both, miscible and immiscible, where miscibility is determined by the minimum miscibility pressure (MMP) estimated from correlations. We compare continuous gas injection and water alternating gas (WAG) injection and decipher how they are influenced by matrix and fracture heterogeneities. The results show up to 7% incremental recovery by gas injection compared to secondary recovery due to the contact of un-swept zones and improved hydrocarbon displacement. We show how gas channelling along high permeability layers is mitigated by WAG injection and that detailed representation of small- and large-scale geological features such as fractures and high permeability streaks, leads to improved prediction of hydrocarbon recovery. Using this understanding, we optimise recovery by ensuring effective gas utilization, injection strategy and miscibility conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".