Origins of hydrogen sulfide (H2S) in gas reservoirs by isotopic analyses: the mitigation of operation risks
Bibliographic record
Abstract
The distribution and origin of hydrogen sulfide (H2S) within gas reservoirs is an important issue due to its toxicity and ability to corrode metal infrastructure, even at low concentrations (i.e. 50 ppm). H2S gas is regarded as a high priority for health and safety at drilling sites. The distribution of H2S, in some basins, can be inexplicable with a mix of sweet (no H2S) and sour (contains H2S) wells within one multi-well pad. Sour gas is a concern in some gas and coal fields in Australia which include Gippsland, Bowen and Cooper-Eromanga basins as well as in the North West Shelf with typical concentrations below 10 000 ppm. For example, the German Creek Formation (Bowen Basin) contains up to 77 ppm of H2S gas and coal seam gas producers will need to perform a risk assessment while exploring and developing this resource. There are multiple sources of H2S gas sulfur and this includes sulfate minerals, pyrite, organic sulfur or from frack water. This research utilises the isotopic variation in the sulfur and oxygen of potential sources, coupled with petrological analyses to determine H2S gas generation. Data is used to predict the gas distribution within the reservoirs to reduce exploration risks. One initial study on the Triassic Montney Formation in western Canada produces H2S gas at concentrations up to 220 000 ppm. Isotopic analyses suggest that the H2S is generated from either Triassic sulfates or a mixture of Triassic and Devonian sources and not solely from Devonian rocks as first expected.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".