Enhanced Numerical Simulations of IOR Processes Through Dynamic Sub-Gridding
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Bibliographic record
Abstract
Abstract Numerical simulations of IOR processes (SAGD, VAPEX,...) often require a very fine space discretization in order to follow steep fronts and to capture the physical phenomena. As a result, these simulations are mostly CPU time consuming. In this paper a new and very efficient approach is presented which allows to reduce the computational time without any loss of precision. This approach, based on a dynamic sub-gridding technique, acts as an external and independent mesh generator and does not rely on any specific simulator. To this respect, it provides an effective alternative tool for numerical performances optimization of recovery processes simulations. In addition, contrary to usual local refinement methods, this approach results in great accuracy, both in terms of fluids productivities and production rates, compared to uniform fine grids results. In the meanwhile, the simulation time is damped up to a factor 6 in the first years of production and slowly reaches a global gain of over 3.5 for long production periods. Then, after explaining the general methodology, a SAGD real field case is introduced as an example of application. For this process, where a large computational time is generally required, significant improvements of performances can be observed. Introduction Since its introduction by Roger Butler in the 80's (1)(2), Steam Assisted Gravity Drainage (SAGD) has gained a large audience among the petroleum industry (3). This is particularly true in Canada where it is one of the most promising process to recover a part of the huge reserves of extra heavy oil and bitumen. In this country, several pilots or field scale implementations of SAGD are currently underway (4)(5) in all of the three Alberta oil sands deposits and in the heavy oil deposit of Saskatchewan. While finally quite simple in its concept, SAGD requires to be modelled carefully with numerical simulators in order to be optimised because reservoirs are generally quite heterogeneous and well trajectories not exactly horizontal. A crucial aspect of SAGD modelling is that most of the fluid displacement takes place at the thin interface between the steam chamber and the cold non contacted oil zone. Therefore, fine cells or gridblocks are necessary at least in the region covered by this interface. Without dynamic sub-gridding, it means that whole the simulated domain must be discretized with fine blocks, which results in a huge number of blocks, especially in 3D, and consequently in very time consuming simulations even with actual computers. The interesting aspect of SAGD, and also of VAPEX a derived process, is precisely the thin interface which exists ahead of the steam chamber, and therefore it seems quite logical to consider dynamic sub-gridding techniques to follow this interface while it moves away from the injector, in order to have fine gridblocks in the region covered by the interface but coarser gridblocks ahead and behind of it. So, the total number of gridblocks would be reduced as well as the CPU time.
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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 it