Bibliographic record
Abstract
Abstract To satisfy the growing global gas demand more reservoirs with sour contaminants (up to 40% of H2S and significant CO2) will be developed. Worldwide more than 1600 TCF of Sour Gas is anticipated. Shell has more than 60 years of experience in sour gas processing, ranging from the first facilities installed in Jumping Pound, Canada, to recent projects under development in Kazakhstan and Oman. This paper will describe a number of challenges and opportunities associated with development of "sour" projects. The fact that H2S is lethal at low concentrations and highly corrosive in the presence of CO2 and/or (salty) water indicates that safety is a main driver in these projects. It is of crucial importance that the H2S is contained and that plant integrity is assured through tightly controlled maintenance programs. Product specificationsfor produced gas and hydrocarbon liquids are ever tightening and legislation on emissions are becoming more stringent. Deep removal of H2S and other sulphur components like mercaptans and carbonyl sulphide is required. This increases the complexity and therefore cost of the sour gas processing facilities, which must compete with production from sweet gas in the region/country or alternatives such as LNG import. Technology innovation as well as smart integration of technologies are essential for the cost effective development of sour gas assets ensuring all specifications and emission requirements are met. Several examples of these technology innovations will be presented in this paper.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".