Numerical Prediction of H2S Production in SAGD: Compositional Thermal-Reactive Reservoir Simulations
Bibliographic record
Abstract
Abstract Nowadays EOR methods such as thermal techniques are widely used to recover the viscous hydrocarbons from heavy oils and bitumen reservoirs. One of the thermal methods is the Steam-Assisted Gravity Drainage (also called SAGD), which consists in injecting steam into the reservoir to melt the viscous oil and allow its mobility. The melted oil falls by gravity to the production well. The injected hot steam, once it reaches the heavy oils/bitumen, induces chemical reactions called aquathermolysis. These reactions generate gases such as hydrogen sulfide (H2S) or carbon dioxide (CO2). The H2S is known to be highly toxic and corrosive. Hence it needs to be given a particular attention when it is produced at the surface. Reservoir models have been built to simulate thermal effects during a SAGD process but only few publications in the literature deal with the aquathermolysis reactions occurring in reservoirs where steam is injected. This paper focuses on building a reservoir simulation model to forecast the H2S production. The example of the Hangingstone heavy oil field in Canada has been chosen. This simulation model is based on a compositional PVT description for heavy oil/bitumen and on a recently developed sulfur-based compositional kinetic model to describe the aquathermolysis reactions. The description of the heaviest components found in heavy oils/bitumen is made through a SARA decomposition. The reactive model that describes the aquathermolysis reactions is firstly presented. Then a section of this paper is dedicated to the building of a PVT model for heavy oil. Another chapter presents the 2D heterogeneous reservoir models used for the simulations. Finally the simulations results are presented. A sensitivity analysis has been performed to investigate the effect of the rock conductivity and the pressure/temperature of the injected steam on the H2S production. The different simulations have given consistent results with production data in terms of H2S production at surface. This shows that both the fluid description and the aquathermolysis kinetic model used in the study are relevant for the prediction of H2S production in the context of steam injection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".