A Mathematical Model to Predict Solvent/Steam Flashing in Pure Solvent or Solvent/Steam Coinjection in Horizontal Producers
Bibliographic record
Abstract
Summary Although the steam assisted gravity drainage (SAGD) process is still the preferred thermal-recovery process method for Athabascan deposits in Alberta, Canada, the interest in solvent-based techniques is growing due to reduce greenhouse-gas (GHG) emissions and water treatment concerns. In SAGD process, the thermodynamic trapping or subcool trapping is quite efficient due to strongly dependency of bitumen viscosity to temperature. As Irani (2018) discussed subcool trapping for solvent applications such NsolvTM recovery process is inefficient due to week dependency of solvent viscosity to temperature. Other factor that effects the efficiency of the thermodynamic trapping is that the pure solvent injection recovery processes are operated at low pressure and it is not large temperature window for operators to apply large subcools. Such challenges make the pure solvent injection recovery processes a perfect case for deployment of Flow-Control-Devices (FCDs). 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. Application of FCDs are even more important in pure-solvent injection recovery processes due to large amount of solvent in the liquid pool and also low latent heat of solvent in comparison of water. With FCDs, the draw-down pressure is typically higher, resulting in flashing near the well bore, which is largely correlated to latent heat of the main fluid in the liquid pool. The flashing creates either steam or vapour breakthrough that causes the reduction in the relative permeability of the liquid phase. Such mobility reduction creates new equilibrium that stabilizes at lower rates. Such new equilibrium analysis is conducted by forcing a new temperature gradient to the model. Such condition creates an environment that leads into extensive solvent-breakthrough called solvent-coning in this study. The main output of such analysis is the produced solvent gas-fraction produced at the sand-face. The gas-fraction is an important input for the flow control devices (FCDs) especially at subcools close to the zero, as it controls its behavior. EoS model is also created and simplified to be possible to used in defining different equilibrium conditions. 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 for application of the pure solvent injection recovery processes. This study is the first of its kind that couple the EoS and Darcy flow in the liquid pool. The model includes all the factors into a liquid-relative-permeability, and limitation of the liquid flow into producer is modeled by Darcy flow and reduction of such relative-permeability.
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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 | 0.001 | 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 | 0.001 |
| 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".