Multi-thermal Fluid Assisted Gravity Drainage Process to Enhance the Heavy Oil Recovery for the Post-SAGD Reservoirs
Bibliographic record
Abstract
Abstract Multi-thermal fluid is a new type of heat-carrier proposed in recent years for the EOR process in heavy oil reservoirs. Compared with the conventional saturated-steam injection process, multi-thermal fluid injection technique combines the multiple advantages of im/miscibility gas injection and thermal recovery. In this paper, based on the multi-thermal fluid injection process and the conventional steam-assisted-gravity-drainage (SAGD) process, a new thermal gravity-drainage process, multi-thermal fluid assisted gravity drainage (MFAGD) technique is proposed to enhance the heavy oil recovery for the post-SAGD reservoir. From the dimensionless scaling criterion of gravity-drainage process, two 3D gravity-drainage experiments (SAGD, SAGD-to-MFAGD) are firstly conducted to explore the EOR mechanisms of multi-thermal fluid in heavy oil reservoirs and oil sands. Subsequently, numerical simulation has been performed to match the experimental measurements. Then, from the scaling criterion, these lab-scale reservoir properties are converted to field-scale. Thus, a field-scale numerical model is developed. From this field-scale numerical model, the difference of SAGD process and MFAGD process are discussed. The reservoir adaptability of MFAGD process are investigated, and the operation parameters are numerically optimized. Experimental results indicate that a strategic combination of SAGD process and MFAGD process could tremendously improve the development of heavy oil reservoirs. And MFAGD process can be adopted as an additional recovery stage for the heavy oil reservoirs after SAGD process. For the mechanisms, with the exception of the conventional thermal recovery mechanisms of steam injection, it is shown that the mechanisms of heat insulation, energy recovery, gas dissolution, foamy oil and auxiliary cleanup are also important for this new thermal gravity-drainage technique. From the lab-scale numerical results, the injection of multi-thermal fluid further unify the chamber profile along the horizontal wellbore. The field-scale numerical results show that compared with the performance of SAGD process, MFAGD process has lower steam consumption and lower cSOR. The steam chamber after MFAGD process is shaped like "liquid drop" instead of the conventional "inverted triangle" shape of SAGD process. In order to obtain a better performance for the MFAGD process in post-SAGD reservoir, the value of Kv/Kh should be not less than 0.3; the reservoir thickness should be not less than 30 m; and the value of NTG should be not less than 0.7. For MFAGD process, the optimal gas/steam ratio (standard condition) is 1:1, steam injection rate is 200 m3/d, and the chamber operation pressure is 3.0 MPa. This paper further deepens the understanding of the EOR mechanisms of multi-thermal fluid injection process in heavy oil reservoirs and oil sands. The proposed MFAGD process will be a significantly potential EOR method for the post-SAGD reservoir.
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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 | 0.000 | 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 | 0.001 |
| 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 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".