Performance Prediction of Solvent Enhanced Steam Flooding for Recovery of Thin Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract More than half of heavy oil reservoirs in Western Canada are less than 5m thick and SAGD is generally not thought to be economically viable for such kind of reservoirs due to lack of Potential Energy of Gravity and significant heat losses to the overburden. Solvent enhanced steam flooding (SESF), a kind of enhanced steam flooding by co-injecting solvent with steam, has shown to be promising in enhancing oil rates in thin reservoirs. In this paper, a semi-analytical model is established for predicting production performance of SESF. The model is mainly built based on Energy Conservation and Fick's Law to predict the steam front position as well as the solvent concentration profile in the reservoir. Besides, the blocking effects of steam condensate on solvent diffusion is modeled by introducing the oil-water two phase flow theory. Then, the model is divided into three parts corresponding to the three production stages of SESF, and they are semi-analytically solved successively. The proposed model is validated by comparing calculated oil production rate with the results of a numerical simulation method. The results indicate that the enhancement of oil production rate mainly happened in the early stage of the process which is achieved by the combining effects of heat and convection-enhanced mixing of solvent and heavy oil. On the basis of a sensitivity analysis for performance of SESF, it is realized that the operating thickness and solubility of a solvent are proportional to steam oil ratio reduction of SESF compared to conventional steam flooding. Besides, a relatively lower injection rate and a longer well spacing may result in higher thermal efficiency increment due to longer contacting time of solvent with crude oil. Piloting SESF in a field has many challenges, especially when considering its main economic factors: production increase and solvent cost. Therefore, it is anticipated that considering the dynamic mass transfer and blocking effect of accumulated condensate on solvent diffusion in SESF process, which are important mechanisms of SESF with inadequate understanding in literatures, the newly developed model will help to better predict and design the future SESF heavy oil recovery projects in thin pay zones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".