On the Partitioning of Hydrogen Sulfide in Oilfield Systems
Bibliographic record
Abstract
Abstract Hydrogen sulfide (H2S) is a toxic, corrosive gas found in many oilfield production systems. While it can be indigenous to both gas and oil fields, it is also generated in real time within the reservoir by sulfate-reducing bacteria as a result of injecting sulfate-containing water during waterflood. Production of this biogenerated H2S is many times unanticipated and consequently causes operational problems associated with corrosion, safety and satisfying pipeline specifications. While measurement of the H2S concentration in gas is relatively straight forward, the quantitative determination of total H2S mass rate actually being produced in multi-phase systems involves knowing the concentrations in the oil and water also. Partitioning of H2S between the oil, water and gas is a thermodynamic process that is a function of the temperature, pressure, fluids composition, and water pH and ionic strength. The effect of pH is especially important, especially when the pH is neutral or basic for high water cut systems. At these conditions the amount of H2S dissociating into HS− and S= ions, which will remain dissolved in the water phase and will not partition to the oil and gas, becomes significant and will not be reflected in measuring only the concentration in the gas. This paper presents the relationships describing the partitioning of H2S and will provide examples of oilfield systems demonstrating the effects of the operational parameters on determining total H2S mass production rates. The H2S partitioning algorithm described in this paper has been incorporated into a reservoir souring forecasting model, which has been used to evaluate the impacts of operational alternatives on H2S production.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".