Geochemical Changes Associated with High-Temperature Heat Storage at Intermediate Depth: Thermodynamic Equilibrium Models for the DeepStor Site in the Upper Rhine Graben, Germany
Bibliographic record
Abstract
The campus of the Karlsruhe Institute of Technology (KIT) contains several waste heat streams. In an effort to reduce greenhouse gas emissions by optimizing thermal power consumption on the campus, researchers at the KIT are proposing a ‘DeepStor’ project, which will sequester waste heat from these streams in an underground reservoir during the summer months, when the heat is not required. The stored heat will then be reproduced in the winter, when the campus’s thermal power demand is much higher. This paper contains a preliminary geochemical risk assessment for the operation of this subsurface, seasonal geothermal energy storage system. We used equilibrium thermodynamics to determine the potential phases and extent of mineral scale formation in the plant’s surface infrastructure, and to identify possible precipitation, dissolution, and ion exchange reactions that may lead to formation damage in the reservoir. The reservoir in question is the Meletta Beds of the Upper Rhein Graben’s Froidefontaine Formation. We modeled scale- and formation damage-causing reactions during six months of injecting 140 °C fluid into the reservoir during the summer thermal storage season and six months of injecting 80 °C fluid during the winter thermal consumption season. Overall, we ran the models for 5 years. Anhydrite and calcite are expected mineral scales during the thermal storage season (summer). Quartz is the predicted scale-forming mineral during the thermal consumption period (winter). Within ~20 m of the wellbores, magnesium and iron are leached from biotite; calcium and magnesium are leached from dolomite; and sodium, aluminum, and silica are leached from albite. These reactions lead to a net increase in both porosity and permeability in the wellbore adjacent region. At a distance of ~20–75 m from the wellbores, the leached ions recombine with the reservoir rocks to form a variety of clays, i.e., saponite, minnesotaite, and daphnite. These alteration products lead to a net loss in porosity and permeability in this zone. After each thermal storage and production cycle, the reservoir shows a net retention of heat, suggesting that the operation of the proposed DeepStor project could successfully store heat, if the geochemical risks described in this paper can managed.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".