Steam injection pressures and rates, variable permeabilities systems, and wells alignments parameterization in SAGD: A simulation study
Bibliographic record
Abstract
SAGD is one of the steam recovery methods of heavy oil, which is typically performed using one horizontal injection well and one horizontal production well. However, multiple injection and production wells might also be utilized. Recently, studies of this method involving steam injection rate and pressure parameterization were performed over limited ranges of these parameters, with limited knowledge of steam injection rate characterization based on different levels of average homogeneity and heterogeneity of permeability systems. Moreover, there are no simulation studies of vertical injectors and multiple horizontal injection wells, or steam injection rates characterization. This paper examines these limitations considering the reservoir and fluid properties of the Liaohe heavy oil field in China. It is revealed that elevating pressures for a given steam injection rate do not have a significant impact on heavy oil recovery performance factors. A recommendation of injection at slightly higher than the initial reservoir pressure (6.3 MPa), which is 6.5 MPa to compensate for steam compression costs. Increasing the steam injection rate within a limited range provides a larger volume for the steam chambers that raise the average reservoir temperature and result in higher oil recovery factors. The positive-rhythm reservoir (permeability rises from the top to the bottom of the reservoir) extracts more oil than that of the negative-rhythm one (permeability decreases from the top to the bottom of the reservoir). The vertical injector has better oil-sweep efficiency compared with the horizontal injector due to more expandable steam chambers with a very small steam chamber rising stage. Two horizontal injectors at the same total steam injection rate as conventional SAGD enhance the oil recovery factor (an increment of up to 15.24 %) and reduce cumulative steam-oil ratio by 3.64.
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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".