Mechanisms causing injectivity decline and enhancement in geothermal projects
Bibliographic record
Abstract
In geothermal projects, reinjection of produced water has been widely applied for disposing wastewater, supplying heat exchange media and maintaining reservoir pressure. Accordingly, it is a key process for environmental and well performance assessment, which partly controls the success of projects. However, the injectivity, a measure of how easily fluids can be reinjected into reservoirs, is influenced by various processes throughout installation and operation. Both injectivity decline and enhancement have been reported during reinjection operations, while most current studies tend to only focus on one aspect. This review aims to provide a comprehensive discussion on how the injectivity can be influenced during reinjection, both positively and negatively. This includes a detailed overview of the different clogging mechanisms, in which decreasing reservoir temperature plays a major role, leading to injectivity decline. Strategies to avoid and recover from injectivity reduction are also introduced. Followed is an overview of mechanisms underlying injectivity enhancement during reinjection, wherein re-opening/shearing of pre-existing fractures and thermal cracking have been identified as the main contributors. In practice, nevertheless, mixed-mechanism processes play a key role during reinjection. Finally, this review provides an outlook on future research directions that can enhance the understanding of injectivity-related issues.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".