To extract geothermal energy from low-permeability reservoirs: Development and simulation of cyclic water injection (CWI)- and water-assisted gravity drainage (WAGD)-based processes
Bibliographic record
Abstract
Geothermal energy is a clean energy source to fulfill the increasing global energy demands. For the first time, several energy extraction strategies are proposed and compared through numerical simulations for effective energy recovery from low-permeability geothermal reservoirs. We simulate geothermal energy extraction using either Cyclic Water Injection (CWI) or Water-Assisted Gravity Drainage (WAGD) processes with or without hydraulic fracturing over a 10-year operation. We evaluate the positioning of injector and producer in WAGD process, well spacing, addition of one injector, and the time variation of injection/soaking periods in CWI process. The simulations indicate that the fractured reservoirs generate more energy than those without fracturing; CWI-based processes exhibit higher energy recovery efficiency than WAGD process in terms of Energy-Water-Ratio (EWR). EWR, as a newly proposed parameter in this study, is defined as the energy production specific to per unit volume of water injected, which is useful in evaluating the cost-effectiveness of a geothermal energy extraction process. The gravitational effect proves to be the dominant factor that determines energy generation compared to phase change in WAGD process. In addition, formation and impacts of steam chambers in CWI and WAGD-based processes are discussed. Furthermore, statistical analyses are performed to evaluate the effects of reservoir temperature, pressure, permeability, and their mutual interactions on cumulative energy production. Eventually, two correlation models for predicting cumulative energy production based on these formation properties are proposed. This study provides a new perspective on implementing different innovative exploration strategies and optimization processes for energy extraction from low-permeability geothermal 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".