An Adaptive Preconditioning Strategy to Speed up Parallel Reservoir Simulations
Bibliographic record
Abstract
Abstract In reservoir simulation, an ILU preconditioner is the most widely used preconditioner for preconditioning linear systems due to its simplicity and low computational cost. However, an ILU preconditioner sometimes is not effective enough, especially for a large-scale parallel reservoir simulation problem with a highly heterogeneous geological model. A constrained pressure residual (CPR) preconditioner is considered a more efficient one, which employs two stages of a preconditioning process: the first stage uses the Algebraic Multi-grid (AMG) method to solve a pressure system, and the second stage uses the ILU method to solve the whole system. Its disadvantage is the high computational cost of the AMG method. In order to reduce the computation costs on preconditioners and the resulting linear solvers, we have developed an adaptive preconditioning strategy [16] to automatically select a preconditioner between an ILU preconditioner and a CPR preconditioner or switch the ILU preconditioner to the CPR preconditioner and vice versa during a linear solution process. In this paper, the adaptive strategy is further analyzed and studied to understand its numerical performance and to choose optimal switch criteria.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".