Reservoir modelling for underground hydrogen storage in the onshore Otway Basin, Victoria
Bibliographic record
Abstract
Lochard Energy, supported by research from CSIRO’s Energy Division, is investigating the feasibility of geological underground hydrogen storage (UHS) in depleted gas fields within the onshore Otway Basin, Victoria. Lochard’s H2RESTORE Project aims to use electricity sourced from the National Electricity Market (likely during periods of high renewable energy generation and low energy demand) to make hydrogen, store it underground as long duration energy storage and then reuse it to generate electricity when demand is high. This paper presents a general overview of a two-stage UHS reservoir modelling approach for porous sandstone reservoirs at two depleted gas fields with contrasting trap geometries and storage objectives. Stage 1 includes the static model building, subsequent dynamic model history matching and conversion to a full compositional model. Stage 2 involves the UHS well placement, setting of hydrogen cycling targets and constraints, initial filling scenario testing and hydrogen cycling analyses. Modelling at Field A was aimed at demonstrating the technical case for UHS feasibility at low hydrogen injection volumes for a pilot project, with acceptable hydrogen purity on production over a minimum number of cycles. Modelling at Field B was designed to demonstrate that a seasonal energy demand profile could be met over a 10-year period within the usual commercial operational constraints associated with cycling gas in underground porous reservoirs. Simulation results were able to demonstrate that hydrogen could be successfully injected and withdrawn at suitable production rates and purity to meet the project objectives at each of the fields.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".