Integration of Geothermal Energy Recovery and Carbon Sequestration of an EGS by CO2-Water Mixtures
Bibliographic record
Abstract
Abstract Enhanced geothermal system (EGS) has been acknowledged as a sustainable and low-carbon alternative for generating electricity. CO2 and water are two conventional heat transmission fluids in an EGS. However, the additional environmental benefits from storing CO2 in the reservoir cannot be achieved in a water-EGS, and an early thermal breakthrough or inadequate power production are the main obstacles in a CO2-EGS. This study introduces a co-injection of CO2 and water in an EGS development. Reservoir and economic models are constructed to compare the technical and economic performance of a water-EGS, a CO2-EGS and a CO2-water-EGS. The results indicate that the proposed CO2-water-EGS can produce more geothermal electric power than CO2-EGS and water-EGS, which can effectively solve the drawbacks of insufficient extraction rate in CO2-EGS, and meanwhile, improve the contribution in CO2 emissions compared to water-EGS. From the economic perspective, a higher Net Present value (NPV), an earlier payback period, a lower breakeven electricity market price, and a lower breakeven carbon credit rate are observed in CO2-water-EGS. Conversely, a not promising electricity generation and larger CO2 consumption make it hard to receive a higher NPV even though the highest carbon credit is earned. In addition, water-EGS performs a similar economic performance in contrast to CO2-EGS since it cannot obtain additional carbon credit although zero investment is required in purchasing expensive CO2. More importantly, CO2-water-EGS is the best option in changeable electricity market price, but CO2-EGS is better in profitability when the carbon credit rate exceeds $50/ton. With a comprehensive comparison of technical and economic feasibilities, this study provides the operators or stockholders with valuable insight into the operation strategy for EGS development.
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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) | 0.001 | 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".