Prediction of Flow Control Devices Performance in SAGD Producers Subjected to Sand Impact Erosion
Bibliographic record
Abstract
Abstract Over the last decade, the Steam Assisted Gravity Drainage (SAGD) thermal recovery process has been successfully commercialized in a range of applications located in the oil sands of Western Canada. Prevention or remediation of SAGD production wells due to slotted liner erosion or high gas/steam production later in the life of the well pair requires a change in basis of design of the completion that is compatible with specific well challenges of these applications and facilitates drainage control while overcoming additional pressure drops due to impaired or plugged-off sand control. Flow Control Devices (FCDs) have demonstrated significant potential for improving recovery in SAGD production wells. However, for most operators the results with FCDs have been mixed or negative, that mostly correlated to either bad design or impact of erosion. A common practice to evaluate erosion is correlation suggested by American Petroleum Institute recommended practice 14E (API-14E). Although due to simplicity API-14E equation became popular, its predictions are quite questionable. Furthermore, quantifying FCD reliability through time and degradation of its performance due to erosion and plugging is key to understand their interaction with reservoir in long-run. Currently distributed temperature sensing (DTS) monitoring are vastly used for SAGD producers to improve the interpretation on their long-term performance. Although DTS helps to identify the hot-spots but it cannot identify steam flashing. In an effort to optimize FCDs operators also required to know the flowrates at each FCD, and till this point distributed acoustic sensing (DAS) acquisitions could not be helpful. This work is a continuation of a previous publication discussing near wellbore flashing in SAGD producers (Irani et al., 2020). As discussed in previous publication the near wellbore flashing causes a reduction in the relative permeability of the liquid phase, which creates a new equilibrium that stabilizes at lower drainage rates. But it also yields to higher steam rates at sandface. Steam inflow has two-fold effect on FCDs performance, firstly, increase in steam quality yields to high chocking, and secondly, it creates sand-instability at the sandface that yields to high erosion rates. In order to design and optimize FCDs for SAGD, it is necessary to characterize different FCDs subjected to erosion using actual field data. In this study, to estimate how FCD performance has evolved with respect to erosion, the mathematical framework is presented that honors both DTS data and measured production evolution during the life of the wells. The predictions of this mathematical model were compared with actual erosion locations that was measured with downhole ultrasound-based imaging technique. And it was only few meters away. Implementation of this type of analysis can help operators in evaluating the effectiveness of different type of FCDs, whether they are primarily momentum- or frictional-based devices.
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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 | 0.001 |
| 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) | 0.001 | 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 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".