Impact of Noncondensable Gas on Performance of Steam-Assisted Gravity Drainage
Bibliographic record
Abstract
Summary This study investigates, by means of numerical modelling, the impacts of naturally occurring and continuously coinjected and intermittently coinjected noncondensable gas (NCG) at different stages of the steam-assisted gravity-drainage (SAGD) process. The CMG Builder software was used to construct a homogeneous 2D baseline model based on generic Athabasca-type reservoir properties and well configuration. A fluid-component model was generated using the CMG WinProp package for modelling the phase behaviour and properties of reservoir fluids. This fluid-component model was modified to incorporate manually calculated K-values for the gas-water- and the gas-bitumen-phase equilibria. The modified fluid model was then incorporated into the baseline model and the CMG STARS thermal reservoir simulator. The simulation results of this study show that methane coinjection along with steam is generally not beneficial. Although it can reduce the heat loss to the overburden to some extent, the reduction in oil-drainage rate and total oil recovery negates the benefits of such heat-loss reduction. The poor performance of NCG addition to SAGD results from the tendency for the NCG to remain in the vicinity of the steam chamber and reduce the transfer of heat into the cold bitumen at the edges of the steam chamber, thereby impeding steam-chamber growth. Accurate modelling of NCG addition to steam in SAGD is highly dependent on the availability of appropriate relative permeability curves and gas-solubility data. NCG with steam may perform better compared with steam-only injection in SAGD if methane coinjection were investigated using a heterogeneous model in which SAGD is affected adversely by the presence of reservoir heterogeneities in the form of shale barriers, inclined heterolithic strata (IHS), and steam-thief zones.
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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) | 0.001 | 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 | 0.001 |
| 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".