Simulating the ES-SAGD Process With Solvent Mixture in Athabasca Reservoirs
Bibliographic record
Abstract
Abstract The ES-SAGD process was developed to improve the energy and oil drainage efficiency of the SAGD process. The idea of the ES-SAGD process is to co-inject solvent with steam and the co-injected solvent mixes with the bitumen to further reduce the viscosity of the heated bitumen along the boundary of the steam chamber thus enhances the oil recovery. Practically, the coinjected solvent will be a solvent mixture (such as diluent /naphtha) due to its availability and reduced cost than a pure hydrocarbon. This paper reports the results of an ES-SAGD lab test conducted with steam and diluent co-injection using Athabasca bitumen. To simulate the ES-SAGD test, a pseudocomponent scheme to represent the complex solvent mixture in the numerical model is derived, based on the diluent composition and measured PVT data. The behaviors and effects of the co-injected solvent in the ES-SAGD process are analyzed through detailed history matching of the ES-SAGD test. Numerical sensitivity analyses are also performed to investigate the effects of some key parameters in the numerical approach. Introduction The Steam Assisted Gravity Drainage (SAGD)1 and the Vapor Extraction (VAPEX)2, combined with the horizontal well technology, are being developed to recover the enormous heavy oil and bitumen resources in Western Canada. The SAGD process has been successfully field-tested and is in the early stage of commercial-scale application while the VAPEX process is still at the piloting stage. Both processes have their advantages and disadvantages. The advantage of the SAGD process is its high oil production rate. However the high production rate of the SAGD process is associated with intensive energy consumption and CO2 emissions from burning natural gas to generate steam, and costly post-production water treatment. The VAPEX process, on the other hand, has the advantage of lower energy consumption and water usage, therefore less CO2 emission and water treatment cost. However, the major drawbacks of the VAPEX process are its relatively lower oil production rate and the additional cost of solvent. The ES-SAGD process was developed3, 4 to improve the energy efficiency of the SAGD process by combining the advantages of the SAGD and VAPEX processes. In the ESSAGD process, small amount of solvent, pure hydrocarbon (i.e. hexane) or hydrocarbon mixture (diluent), is co-injected with steam. The basic idea is that as the solvent flows with steam along the boundary of the vapor chamber, it dissolves into and mixes with the bitumen, hence reducing the viscosity of the bitumen and further enhances the oil recovery. Practically, the co-injected solvent will be a hydrocarbon mixture (such as diluent /naphtha) due to its availability and reduced cost than a pure hydrocarbon. Thus the study of the impact of the different components in the hydrocarbon mixture in the ES-SAGD process becomes important. In this study, one ES-SAGD lab test with Athabasca bitumen and a field solvent mixture (diluent) as the co-injected solvent using a 2D high-pressure/high-temperature test facility was conducted. The ES-SAGD test was numerically history matched.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".