Determination of Optimal Distance Between Wells in SAGD and VAPEX Methods Using Reservoir Simulation
Bibliographic record
Abstract
Abstract Energy resource extraction is getting more difficult and using the enhanced and advanced level of technology for oil production is getting more common. Steam-assisted gravity drainage (SAGD) and Vapor extraction (VAPEX) are two favorable methods of EOR in heavy oil reservoirs. However, there are some obscure points about these methods, like optimum distances between wells and the overall cost of each method. The main objectives of this project were to find the best distance of wells in these types of EOR methods and to enhance the production of heavy oil reservoirs by SAGD and VAPEX methods, taking into consideration of the economic aspects of this project. In this project, SAGD and VAPEX methods were modeled to increase the production rate. Each stage of the Enhance Oil Recovery (EOR) was simulated and modified through many scenarios in terms of the injection patterns, production & injection wells’ locations as well as the adjustment of different injection steam injection and solvent injection. This project analyzed the SAGD method in the shallow reservoir (RF of 69.8%), and in the deep reservoir (RF of 38%). In addition, the maximum allowable depth to apply the SAGD method was found in this study (3500ft). Based on the calculation from the software, it was observed that the heat loss for this reservoir was around 350°F. The cumulative oil production of the SAGD method in the shallow reservoir was 3.87×10^6 bbl and the deep reservoir was2.1×10^6 bbl, and it for the VAPEX method at the shallow reservoir was 2.74×10^6 bbl, and at deep reservoir around 2.64×10^6 bbl, based on the results of each method the VAPEX method remain with the same quality at both reservoirs, however, the SAGD has the lower production in the deep reservoir which means this method is not suitable for the deep reservoir. Furthermore, the profit of each method was calculated; the profit of SAGD and VAPEX were (190.97 MM$ & 168.97 MM$) at the shallow reservoir, and (72.97 MM$ 162.32 MM$) at the deep reservoir for each process. Finally, the spacing between injection and production wells was obtained at different distances and the best distance was 30 m.
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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 | 0.001 |
| 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) | 0.000 | 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".