Investigation of Methodology to Degrade Miscibility in Compositional Simulation
Bibliographic record
Abstract
Summary Benchmarking the Ultimate Recovery of a field with producing reservoir analogues is required in the industry to support and increase confidence in the resources evaluation. This paper presents a novel method to mitigate or deactivate miscibility effects in coarse 3D miscible gas compositional simulations. The proposed straightforward method allows the enrichment of an analogue database by including oil fields exhibiting either a lower degree of miscibility or in immiscible conditions. When pressure increases, the gas-oil interaction becomes so strong that gas/oil relative permeabilities (G/O KRs) have minimal impact. As a consequence, the impact of miscibility is often overestimated in coarse grid simulations. This is partly due to the assumption of instantaneous G/O equilibrium and also uniform composition in the grid block. Black oil simulation allows to slow down the G/O interaction easily, but similar keywords are not present in commercial compositional simulators, making such a correction much more challenging. But in miscible flooding compositional simulation, the strength of gas-oil interaction is such that this correction is more needed, but also more difficult to perform than in immiscible simulation. Some existing methods are listed, with their drawbacks and limitations. This current methodology works on the Binary Interaction Coefficients, progressively hindering the gas-oil interaction when they are increased, and thus degrading the miscibility behavior. This work allows to degrade the miscibility not only of pure gases, like published before, but also of gases with complex composition. It also allows to keep a 3D trend in the original oil composition, as frequently needed on fields with an initial compositional gradient The method was tested over a deep offshore green field, planned to be developed with total gas reinjection using a WAG injection scheme. The method allowed to compare resources for miscible gas with immiscible gas injections, quantifiying the miscibility impact on recovery, providing confidence in the evaluations. It works both for continuous and alternate injections, allowing to benchmarck miscible WAG with near-miscible WAG and immiscible WAG. This method is allowing a progressive correction, thus allowing it to be used for upscaling, depending on grid size: no correction on very fine grid, a minor correction on fine grid, and a strong correction on coarse grid.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".