Surface Facilities Considerations for the Production of a Large Sour Gas Resource and the Injection of Sour and/or Acid Gas
Bibliographic record
Abstract
Abstract An on shore reservoir containing a large volume of lean sour gas with hydrogen sulfide and carbon dioxide concentrations of 30 mole % and 10 mole % respectively was considered for production. A study was conducted to determine the feasibility of producing this gas and injecting the sour gas or the acid gas components in adjacent reservoirs. Production, processing, and injection of this gas requires extensive surface facilities. Equipment is required to handle the drop out of elemental sulphur that is expected to occur as the gas is produced. Large gas sweetening units are required, as well as large sour gas compressors. Dehydration of the sweet gas stream is required and dehydration of the injection gas stream is also proposed. Energy integration and heat recovery is an important consideration in the overall plant design. This paper describes the required surface facilities and presents the process, material and energy balance considerations that have been incorporated in the design. Study Overview Significant undeveloped sour gas reserves exist in Abu Dhabi. A feasibility study was conducted in ADNOC with the aim to increase sweet gas availability by unlocking these resources. The sweet gas could be the product of sour gas treating, the result of displacing current sweet gas injection volumes, or a combination of the above. The source reservoir under study contains various zones with different conditions, but the average conditions of the sour gas reservoir used in this study are 380 bara and 142°C. The average dry gas composition of the reservoir is:55.9 % methane,33.2 % H2S,9.5 % CO2,1.0 % hydrocarbons in the C2-C7 range, and0.4 % nitrogen. Trace quantities of carbonyl sulfide and mercaptans are also present. The reservoir fluid is assumed to be saturated with water at the reservoir conditions. A calculated water content of 29.4 g/m3 was used. Experience and theoretical considerations indicate that the reservoir fluids are likely to contain elemental sulphur that will drop out of the fluid as it is produced. The well tests did not specifically look for elemental sulphur, but there is anecdotal evidence that the well test tubing was plugged during testing. A conservative design approach was taken, assuming that the reservoir fluid is saturated with elemental sulphur at reservoir conditions. Calculations based on data from Alberta Sulphur Research1 indicate a sulphur saturation of 7.5 g/m3. Reservoir volume estimates and production engineering work indicated that an initial raw gas flow rate of 22.5 106 m3/d would be optimal for the project. Sweet gas from the project has to meet the normal pipeline specifications of:maximum H2S 20 ppmv,minimum LHV 33.7 MJ/m3,hydrocarbon dewpoint at 39.2 bara of -5°C,water dewpoint at 39.2 bara of -30°C, andthere is no maximum total sulphur specification.
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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) | 0.000 | 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".