Thermo‐Hydro‐Mechanical Behavior of Saturated Porous Media Under Non‐Isothermal Flow With Fine Particle Migration
Bibliographic record
Abstract
ABSTRACT Clogging of reservoir formations, known as permeability damage, and wellbore clogging due to mobilization and straining of in situ fine particles are critical challenges in enhanced geothermal systems. This study presents a novel fully coupled thermo‐poro‐elastic model to predict the thermo‐hydro‐mechanical (THM) response of saturated porous media containing fine particles during fluid injection and production operations. The model incorporates transient state fluid flow to capture the coupled effects of pore pressure, temperature changes, stress variations, and fines migration. Fine particles are considered monolayered and size‐distributed, and the concentration of attached fines on the solid skeleton follows the modified particle detachment model. A finite element framework is developed to simulate the reservoir response, incorporating the fine migration effects, with a new expression for well impedance accounting for transient‐state fluid flow. Results reveal that the permeability damage zone surrounding the wellbore expands over time, reducing minimum permeability to 13% of its original value after only 5h. Fine migration significantly alters pore pressure and effective stresses, leading to increased well impedance. Temperature variations influence pore pressure distribution and well impedance evolution through two mechanisms: altering fluid viscosity and inducing solid skeleton deformation, and triggering fines migration and associated permeability damage. These findings provide critical insights into reservoir behavior and strategies for optimizing geothermal energy production.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".