Profitability Evaluation of a Hybrid Geothermal and CO2 Sequestration Project for a Coastal Hot Saline Aquifer.
Bibliographic record
Abstract
With growing interest in commercial projects involving industrial volume CO2 sequestration, a concern about proper containment and control over the gas plume becomes particularly prominent. In this study, we explore the potential of using a typical coastal geopressured hot saline aquifer for two commercial purposes. The first purpose is to harvest geothermal heat of the aquifer for electricity generation and/or direct use and the second one is to utilize the same rock volume for safe and controlled CO2 sequestration without interruption of heat production. To achieve these goals, we devised and economically evaluated a scheme that recovers operational and capital costs within first 4 years and yields positive internal rate of return of about 15% at the end of the operations. Using our strategic design of well placement and operational scheduling, we were able to achieve in our numerical simulation study the following results. First, the hot water production rates allowed to run a 30 MW organic Rankine cycle plant for 20 years. Second, during the last 10 years of operation we managed to inject into the same reservoir (volume of 0.8 x 109 m3) approximately 10 million ton of the supercritical gas. Third, decades of numerical monitoring the plume after the end of the operations showed that this large volume of CO2 is securely sequestrated inside the reservoir without compromising the caprock integrity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".