Experience In Onshore and Offshore CCS Development
Bibliographic record
Abstract
Abstract Over the past fifteen years Shell have explored for, and developed, onshore storage at Quest in Alberta, Canada, and progressed offshore storage in a depleted field to final investment level. We are now developing new projects both onshore and offshore, in aquifers and depleted fields. This paper describes the projects and the integrated value chains – and highlight the differences between developing hydrocarbon resources where the subsurface is the upstream and the prime driver of value, and CO2 storage resources where the subsurface is effectively the downstream, and value is derived from the CO2 sources which are now the "upstream". The experience in developing regulations and projects in partnership with regulators is then outlined and linked to the subsurface technical work. We describe how the technical work builds upon and extends normal hydrocarbon workflows. Key aspects of exploring for saline formation/aquifer storage and depleted field storage are discussed and some of the key challenges of managing the CO2 phase envelope in depleted fields are described. The balance of uncertainty between aquifer storage and depleted field storage is explained, with particular emphasis on the often-neglected challenge of inaccessible wellbores. We have successfully developed onshore storage that, by the time of the conference, will have injected over 7Mt of CO2 sourced from decarbonisation of hydrogen manufacture. In the UK we developed and submitted storage permit applications, and full FEED studies were executed, for depleted field storage. However, this project was halted when Government priorities changed. This has formed the groundwork for multiple new operated projects in North America and Europe. The startup and injection experience from onshore storage in Quest in Alberta, Canada will be presented. Developing CCS projects relies on many of our petroleum engineering and geoscience domains of expertise and can, at first glance, look similar, but there are key differences, for example in containment, capacity, phase behaviour, sustained injectivity, and permitting. These novel aspects must not be neglected and require new analytical approaches. Many new entrants appear to be underestimating these challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.029 | 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 teacher head, 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".