Exhaustive Review of CO<sub>2</sub> Sequestration in Depleted Hydrocarbon Reservoirs: Recent Advances, Challenges and Future Prospects
Bibliographic record
Abstract
Aging or depleted hydrocarbon reservoirs (AHRs or DHRs) represent a promising alternative for CO 2 geo-sequestration compared to other geological formations, owing to their distinctive characteristics and the availability of pre-existing infrastructure. However, large-scale deployment faces complex, multidimensional challenges that require ongoing research to ensure optimal efficiency and safety. Despite notable progress in understanding the technical processes, significant techno-economic barriers remain. To overcome these obstacles, it is essential to adopt a critical and holistic analysis of existing studies while also exploring innovative approaches. Most recent reviews, though contributing significantly, have focused on specific aspects of CO 2 storage in these reservoirs, neglecting a systemic and multidimensional approach that integrates these various challenges into a single analysis. This fragmented approach leaves a gap in the literature, which may result in an incomplete understanding of the complex interactions between different factors, reducing the effectiveness of proposed solutions and limiting the ability to anticipate long-term impacts on the safety and sustainability of sequestration systems. Additionally, the rapid evolution of technology and scientific knowledge necessitates a constant update of studies related to sequestration in DHRs. Incorporating the latest technological innovations and methodological approaches is crucial to optimizing carbon capture and storage (CCS) processes, enhancing long-term safety, and adapting reservoir management strategies to increasing environmental and economic constraints. This review aims to address these gaps by providing a critical, comprehensive, and multidimensional analysis of recent advances while identifying persistent challenges. The integration of technical, economic, and environmental dimensions into a unified perspective offers a strategic global vision essential for guiding future research and supporting industrial applications. Furthermore, synthesizing the most recent developments and highlighting areas requiring further investigation, this study outlines a strategic roadmap for optimizing CO 2 sequestration in AHRs and DHRs, offering crucial insights for both research and industrial innovation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".