Exploring the Feasibility of Geological Storage of Hydrogen in Indian Porous Media: Challenges, Opportunities and the Way Ahead
Bibliographic record
Abstract
Abstract While hydrogen (H2) continues to attract major attention by Indian policymakers and energy analysts as a future sustainable low-carbon energy source that holds immense potential for decarbonizing power, mobility and industrial sectors, inadequate attention has been paid to address the non-uniformity in H2 supply and power demand cycles. This paper aims to investigate the feasibility of long term storage of hydrogen in porous media to address this non-uniformity. Current research indicates that renewables like solar and wind can be utilized to generate H2 which can then be used to fuel our industrial growth. However, renewables, by their very fundamental nature, are prone to fluctuations in energy generation and may not adequately ensure energy availability at all hours of the day or all seasons of the year. This is even more challenging for a country like India where access to energy must be cheap, sustainable and reliable. For this, ongoing research has focused on generating H2 during the peak intervals (when renewables operate at peak efficiency) and storing it for use during the lean hours (when renewable energy generation capacity is down). The large scale storage of H2 can be accomplished in porous geological media (subsurface H2 storage. SHS), primarily in saline aquifers and oilfields which have been depleted. This would enable planners to balance seasonal discrepancies between energy demand and supply. However, the various processes behind SHS remain poorly understood. Thus, in this study, the various challenges associated with SHS such as inferior front formation, H2 mobility control, maximum storage depth, maximum storability depth, microbial corrosion and permeability changes have been identified and solutions to them have been proposed. Also, to mitigate these challenges, a list of Indian reservoirs have been identified and proposed for SHS. A future course of action has been drawn for Indian policymakers to suitably promote SHS shortly, enabling its large-scale safe and efficient deployment on a nation-wide scale. The paper adds value to the wider scientific community by introducing the relatively new and less well understood concept of subsurface hydrogen storage. It is expected that the information presented in this study will enable faster assimilation and adoption of cleaner hydrogen fuel source while allowing the hydrocarbon industry to leverage their expertise in the coming future.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".