Parallel Simulation of Full-Field Polymer Flooding
Bibliographic record
Abstract
In recent years, polymer flooding has become a mature technology to improve the reservoir recovery rate and has been successfully implemented in many oil fields. Because of high operating costs of a polymer flooding project and the impact to the environment, the polymer injected per unit volume should be tracked for each polymer flooding pattern. Hence, the full-field reservoir simulations with fine-scale grids are needed to capture fine-scale phenomena and to optimize the process. To meet these objectives, a polymer module has been developed in our in-house parallel black oil simulator, which allows parallel simulations using clusters and supercomputers. With our parallel simulator, the elapsed time of full-field simulations with millions of grid blocks can be reduced from days to hours or minutes, and even for a much larger model with hundreds of millions ofgrid blocks, the simulations can be finished in practical time. In order to guarantee the computational efficiency and the parallel scalability, an inexact Newton method is applied and a new CPR (Constrained Pressure Residual)-type preconditioner is designed. In this paper, a SPE10-based polymer flooding case is tested, the performance of nonlinear and linear solvers are robust, and an encouraging parallel scalability is obtained. The second case is a full-field polymer flooding case, for which a coarse grid model anda fine grid model are both used. The results show the differences of the oil production rate and the water cut, which illustrates the accuracy of fine-grid simulations. For the fine grid model, up to 1024 CPU cores are employed, and an excellent speedup is achieved.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".