CCS coupled with CO2 plume geothermal operations: Enhancing CO2 sequestration and reducing risks
Bibliographic record
Abstract
The transition to a low-carbon economy is essential for mitigating climate change, particularly in hard-to-abate sectors. Carbon Capture Utilisation and Storage (CC U S) is expected to play a pivotal role in this transition. This numerical study integrates CO 2 Plume Geothermal (CPG) systems with conventional CCS using field data from the Aquistore CCS project. By employing an integrated subsurface-surface modelling workflow, we simulate and compare two 30-year scenarios with nearly identical masses of sequestered CO 2 : a) “Business-as-usual” CCS and b) coupled CPG-CCUS The results suggest that coupled CPG-CCUS operations provide a stable source of geothermal energy, which could potentially reduce or offset energy costs, such as those associated with the CO 2 capturing process. Additionally, coupling CPG with CCS enhances CO 2 sequestration efficiency by increasing CO 2 mass density in reservoir regions that become thermally depleted due to the sustained injection of CO 2 at temperatures lower than the native reservoir temperature. Although thermally depleted regions develop during both CCS and CPG-CCUS operations, they are significantly more pronounced during the latter due to the combined effect of both cold CO 2 injection and heat extraction. Moreover, CPG‑CCUS operations result in a more concentrated CO 2 plume around the wells. While the production well induces a pressure gradient, this gradient primarily directs fluid flow along the injection-to-production well axis, effectively focusing the CO 2 plume and limiting widespread lateral diffusion of the fluids (brine and CO 2 ) to the far-field reservoir. This localised CO 2 accumulation improves CO 2 plume control and reduces risks associated with uncontrolled CO 2 migration, thereby enhancing the predictability of CO 2 accumulation. This synergistic combination of CCS and CPG operations offers a pathway for the energy transition, enhancing both the CCS technology and the geothermal resource potential, while improving CO 2 sequestration safety.
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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 | 0.000 | 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".