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Record W4394745886 · doi:10.1007/s40789-024-00676-y

Development and technology status of energy storage in depleted gas reservoirs

2024· article· en· W4394745886 on OpenAlex
Jifang Wan, Yangqing Sun, Yuxian He, Wendong Ji, Jingcui Li, Liangliang Jiang, María José Jurado

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 Journal of Coal Science & Technology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of Calgary
FundersPhilippine Council for Industry, Energy, and Emerging Technology Research and Development
KeywordsCompressed air energy storagePetroleum engineeringEnvironmental scienceFossil fuelSortingEnergy storageWaste managementEngineeringComputer sciencePower (physics)

Abstract

fetched live from OpenAlex

Abstract Utilizing energy storage in depleted oil and gas reservoirs can improve productivity while reducing power costs and is one of the best ways to achieve synergistic development of "Carbon Peak–Carbon Neutral" and "Underground Resource Utilization". Starting from the development of Compressed Air Energy Storage (CAES) technology, the site selection of CAES in depleted gas and oil reservoirs, the evolution mechanism of reservoir dynamic sealing, and the high-flow CAES and injection technology are summarized. It focuses on analyzing the characteristics, key equipment, reservoir construction, application scenarios and cost analysis of CAES projects, and sorting out the technical key points and existing difficulties. The development trend of CAES technology is proposed, and the future development path is scrutinized to provide reference for the research of CAES projects in depleted oil and gas reservoirs. Graphical abstract

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.280
Teacher spread0.269 · 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