An Efficient and Parallel Scalable Geomechanics Simulator for Reservoir Simulation
Bibliographic record
Abstract
Abstract A tetrahedron-grid based parallel geomechanics simulator is developed and presented 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. This simulator aims to describe rock matrix deformation and its interactions with pore fluid. In the consideration of the feasibility in coupling with a variety of existing reservoir simulators, the modularized geomechanics simulator is designed to be a library. Through interfaces provided by the library, conventional reservoir simulators can get geomechanics effects involved. In this paper, the framework of a parallel geomechanics simulator and the strategy for coupling with a reservoir simulator are presented. Iteratively coupling approach is employed to make geomechanics modeling more independent and flexible. The procedure for solving solid mechanism and calculating coupling parameters is general, which can be applied to more complicated constitutive laws and rock property descriptions. A parallel strategy is proposed to improve the computational efficiency of solving the coupled problem. To verify the utility and efficiency of the geomechanics simulator, simulations coupled with a three-phase black oil model are performed. Expected geomechanical phenomena are illustrated by numerical experiments. In addition, for testing the scalability behaviour, field scale problems with millions reservoir and geomechanics grid blocks are performed. We use an increasing number of processors to run the cases, respectively, and the results indicate an encouraging speedup.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".