Assessment of H2S Production Risks in Heterogeneous Reservoirs Using Laboratory-Calibrated Compositional Thermal Reactive Simulations
Bibliographic record
Abstract
Abstract SAGD is commonly used as a thermal EOR method to produce heavy oil. However it suffers from the production of acid gases formed by aquathermolysis chemical reactions that occur between the steam, the sulfur-rich oil and the mineral matrix. The objectives of this paper are to take advantage of a comprehensive chemical model coupled to compositional thermal reservoir simulations to predict and understand the H2S production variation at surface according to the type of reservoir. Thermal reservoir simulations coupled to both a SARA based 10-component / 5-reaction chemical model fully calibrated against laboratory data and a compositional PVT are used to simulate SAGD processes on heavy oil fields in Athabasca, Canada. Numerical results are then analyzed to provide a comprehensive analysis of the mechanisms leading to in-situ H2S generation and its production at wellheads based on compositional thermal simulations coupled to a fully laboratory calibrated SARA-based chemical model. Composition of the pre-steam, post-steam and produced oil are compared to understand the effect of the aquathermolysis reactions. The impact of heterogeneities on H2S production both in-situ and at surface can also be observed and explained, especially the variations in vertical permeability. Then simple reservoir models with two facies are used to further understand the impact of heterogeneities on H2S production at surface. Overall heterogeneous cases show important changes in the temperature distribution, fluid flows, reactions kinetics and steam chamber shape that lead to H2S production variations at surface. This detailed description of the involved mechanisms in acid gases production will allow operators to better forecast their H2S risks according to their reservoir properties.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".