Parameterized Local Reduced Order Model of Stimulated Volume Evolution in Reservoirs
Bibliographic record
Abstract
ABSTRACT Real‐time simulation of large‐scale geomechanics problems, such as hydraulic dilation stimulation, is computationally expensive as they must span multiple spatial and temporal length scales, often including nonlinearities and thermo‐hydromechanical processes. This paper introduces a novel local reduced order model (LROM) to enhance computational efficiency for nonlinear and fully‐coupled hydromechanical simulations. The model employs finite element analysis of a two‐dimensional deformable porous media with Drucker–Prager plasticity and stress‐induced permeability enhancement models to describe behavior of sandstone. LROM combines various reduced order models (ROMs), including proper orthogonal decomposition‐Galerkin (POD‐G) to reduce number of degrees of freedom (DoFs), discrete empirical interpolation method (DEIM) to accelerate computation of nonlinear terms, and local POD and local DEIM (LPOD/LDEIM) for further performance enhancements. LPOD and LDEIM classify parameterized training data, obtained from offline coupled full order model (CFOM) runs, into multiple subspaces with similar dynamic features. A new strategy for clustering and classification techniques that align with coupled formulation framework is proposed. The advantages of LROM are demonstrated in a large‐scale application: hydraulic dilation stimulation. LROM exhibits stable, accurate, and efficient online phase, while ROM built with classical POD/DEIM lacks efficiency and stability in Newton–Raphson solver. First, performance of LROM, parameterized by hardening modulus and initial permeability, is evaluated for inputs within training domain. Under CFOMs with DoFs, LROM speed‐up is 400 times. LROM is then parameterized by three inputs, including injection rate and two material properties. Results show that LROM maintains efficiency even for injection rates that extend beyond the training regime.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".