Characterizing the Effects of Lean Zones and Shale Distribution in SAGD Recovery Performance
Bibliographic record
Abstract
Abstract Performance of steam-assisted gravity drainage (SAGD) is influenced significantly by the distributions of lean zones and shale barriers, which tend to impede the vertical growth and lateral spread of a steam chamber. Previous works in the literature have partially addressed their effects on SAGD performance; however, a comprehensive and systematic investigation of the heterogeneous distribution (location, continuity, size, and proportions) of shale barriers and lean zones is still lacking. In this study, numerical simulations are used to model the SAGD process. Capillarity and relative permeability effects, which have been ignored in many previous simulation studies, are incorporated to model bypassed oil. A ranking scheme based on cumulative oil production and cumulative steam oil ratio is devised. A detailed sensitivity analysis is performed by varying the location, continuity, size, proportions, and saturation of these heterogeneous features. Lean zones and shale lens (imbedded in a region of degraded rock properties) with different thickness and degree of continuity are placed in areas located either above the injector, below the producer, or in between the well pair. It is noted that among numerous parameters that influence the ultimate recovery, remaining bypassed oil, chamber advancement, and heat loss, continuity and position of these features in relation to the well pair play a particularly crucial role. We subsequently employ neural network modeling for constructing data-driven models to identify and propose a set of input variables for correlating relevant parameters or measures, which are descriptive of the heterogeneity and properties of the shale barriers, to recovery and ranking results. This work provides a guideline for assessing the impacts of reservoir and saturation heterogeneities on SAGD performance. We identify a set of input variables and parameters that have significant impacts on the ensuing recovery response. The proposed set of variables can be defined readily from well logs and applied immediately in data-driven models with field data and scale-up analysis of experimental models to assist field-operation design and evaluation. The approach presented in this paper can also be extended to analyze other solvent-assisted SAGD processes.
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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".