Coupled Geomechanics and Reservoir Flow Modeling on Distributed Memory Parallel Computers
Bibliographic record
Abstract
A geomechanical model and a multiphase black oil model are iteratively coupled in this paper. Parallel computing is employed to handle large scale problems by benefiting from its features of distributed memory storage and efficient runtime reduction. The finite element method and the finite differencemethod are employed to discretize the two models, respectively. The geomechanical model is developed with the capability of simulating the rock matrix deformation with complex constitutive laws and its effects on reservoir properties. The multiphase flow model is modified by introducing geomechanical variables in a conventional flow model. A coupling strategy is carefully proposed to enable tight and dynamic interactions between these two models, as well as improving parallel computational efficiency. Example problems are presented to demonstrate the utility and efficiency of the coupled models. Expected geomechanical phenomena are illustrated by numerical experiments and validated by commercial software. In addition, for testing the scalability behaviour, field scale problems with millions reservoir and geomechanical grid blocks are performed. The results show encouraging speedups which indicate the integrated models can be an efficient and useful tool for evaluating and analyzing oil and gas production of stress-sensitive reservoirs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".