Sulfur Disposal by Acid Gas Injection: A Road Map and a Feasibility Study
Bibliographic record
Abstract
Abstract Sour natural gas contains H2S and CO2, which have to be removed to meet specifications for sales gas. The removal process is done at the gas plants. The resulting acid gas streams (primarily H2S and CO2) are processed in sulfur recovery units or sulfur plants, which convert the H2S to elemental sulfur instead of burning it and flaring the produced SO2. The sulfur recovery units (SRU) are not a major revenue generator (due to the low sulfur price) and are primarily installed for environmental reasons. The total world sulfur production is anticipated to increase in the future, causing a downward pressure on sulfur prices. With low sulfur prices and large stockpiles of sulfur, it is worthwhile to consider alternate processes for handling sulfur. One such alternate is the injection of acid gas into a subsurface reservoir, much like injecting the produced water during crude oil production. Acid gas injection has several advantages and disadvantages. Advantages include low operating expenses, reduced sulfur emissions into the atmosphere, CO2 sequestration and the ability to handle wide range of acid gas compositions. Disadvantages include finding a geologically isolated disposal or storage reservoir, increased safety risks, subsurface migration and lost revenues from sulfur. Any one of these could be the controlling factor, and a detailed economic and environmental analysis is needed to decide whether an acid gas injection (AGI) scheme should be installed in lieu of sulfur recovery plant. The existing AGI schemes are primarily installed in Canada (a few in the USA) due to low sulfur prices, increased environmental regulations making it mandatory for operators to control sulfur emissions into the atmosphere, and availability of suitable depleted oil and gas reservoirs. This paper presents a roadmap for acid gas injection schemes including the technical and economic factors that need to be addressed in deciding whether AGI is feasible. An example feasibility study for a Saudi Arabian field is included as a case study.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".