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Record W4213152234 · doi:10.2523/iptc-22320-ms

Exploring the Feasibility of Geological Storage of Hydrogen in Indian Porous Media: Challenges, Opportunities and the Way Ahead

2022· article· en· W4213152234 on OpenAlex
Shruti Joshi, Krishna Raghav Chaturvedi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Petroleum Technology Conference · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRenewable energyEnergy storageEnvironmental scienceFossil fuelHydrogen storageEnvironmental economicsElectricity generationEnergy supplyNatural resource economicsWind powerBusinessProcess engineeringPower (physics)HydrogenWaste managementEngineeringEnergy (signal processing)Electrical engineeringEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.137
GPT teacher head0.278
Teacher spread0.140 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it