Productivity Index for SAGD Producers During Steam-Flashing: Coning Model I and II
Bibliographic record
Abstract
Summary Over the last decade, steam assisted gravity drainage (SAGD) process has been successfully commercialized in Alberta and Saskatchewan thermal projects. Remediation of slotted liner wells in cases of erosion, or high gas/steam production later in life requires a different basis of design based on specific well challenges and will be challenged by the additional pressure drops due to poor or variable sand control productivity. Flow Control Devices (FCDs) have demonstrated significant potential for improving recovery in SAGD production wells. FCD experience in SAGD has been primarily positive and most producers performed better with FCDs. While for some operators the results are mixed or negative. The first generation of FCDs deployed in SAGD projects were not designed for such process and in some cases ill-suited. Furthermore, current modeling methods deployed have significant limitations that prevent appropriate FCD design or understanding, and although they can be history matched, typically do not provide useful insight into the pressure drops encountered or held understand the benefit of different devices. In order to design and optimize FCDs for SAGD, it is necessary to characterize different FCDs under the steam-breakthrough condition, and accurately model the flashing in the near wellbore area associated with low-subcool operation. The extensive chocking in FCDs, far greater than initial design, in many cases is due to near-wellbore flashing. This work is a continuation of three previous parts discussing the liquid pool modeling for SAGD producers (Irani, 2018, 2019 and Irani and Gates, 2018). The purpose of this work is to create a PI that fit for purpose of SAGD liquid pool pre- and post-flashing that mainly can be used for analysis and optimization of FCDs. With FCDs, the draw-down pressure is typically higher, resulting in flashing near the well bore. If there is flashing in the near wellbore area, the temperature gradient within liquid pool yields the saturation curve. The flashing causes the reduction in the relative permeability of the liquid phase, that creates new equilibrium that stabilizes at lower rates. Such new equilibrium analysis is conducted by forcing a new temperature gradient to the model. The main output of such analysis is the produced steam quality at the producer sand-face. The steam quality is an important input for the flow control devices (FCDs) especially at subcool close to the zero, as it controls its behavior. This type of analysis can help the operators evaluate the effectiveness of different type of FCDs, whether they are primarily momentum- or friction-style devices.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".