A Geomechanics-Coupled Embedded Discrete Fracture Model and its Application in Geothermal Reservoir Simulation
Bibliographic record
Abstract
Abstract Geomechanics plays an essential role in fluid/heat flow by affecting hydraulic parameters. This influence could be amplified when fractures exist in the system because fracture aperture is highly sensitive to stresses. Coupled fluid/heat flow and geomechanics model is considerably important in simulating thermal-hydrologic-mechanical process, such as geothermal reservoir development. At the same time, due to the rock matrix shrinkage or expansion, thermal stress exerted on fracture surface remolds the aperture significantly and should be incorporated in modeling heat related process. In this study, a coupled fluid/heat flow and geomechanics model, TOUGH2-THM, was developed based on the parallel framework of TOUGH2-CSM, with stress tensor components as primary variables. This modification is aiming on computing normal stresses on discrete fracture surface such that fracture related parameters can be fully coupled with geomechanical model. Embedded discrete fracture model was also improved to be compatible with the geomechanical coupling. Both of TOUGH2-THM and modified EDFM were validated for further application. A geothermal reservoir simulation is conducted by the newly developed model, demonstrating the capability of this program to perform coupled modeling. It is also concluded that geomechanics and especially temperature alteration induced stress could affect fluid/heat flow in fracture and rock matrix. Thus, production efficiency could be impacted as well. The thermal stress generated by temperature reduction could enhance the fracture permeability in orders of magnitude. Various scenarios of injection temperature were modeled and compared. It can be observed that geothermal reservoir development is negatively influenced by geomechanical (and thermal) effect on fractures. The coupled model is helpful to improve the simulation accuracy.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".