Field Implementation of Solvent Aided Process
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Bibliographic record
Abstract
Abstract The authors have previously described a Solvent Aided Process (SAP) that aims to combine the benefits of SAGD and VAPEX. In SAP, a small amount of hydrocarbon solvent is introduced as an additive to the injected steam during SAGD. While steam is intended to be the main heat-carrying agent, the solvent will dilute the oil to reduce its viscosity over and above what is accomplished by heating alone. The overall effect should be improved oil to steam ratio (or reduced energy intensity). Although promising based on the authors' calculations, the process has not been previously applied or tested on a field scale. This paper describes implementation of a SAP pilot at PanCanadian's Senlac Thermal Facility. In addition to dwelling on some of the important parameters of a SAP test, It discusses the design considerations for the field pilot and the necessary modifications to an existing SAGD plant, specifically in the area of boiler operations controls. Although, the design calls for an assessment of reservoir performance results on a longer-term basis, initial results from this pilot look very encouraging. The oil rates have shown a substantial increase and the steam oil ratio, a corresponding derease. This paper also discusses directional economics with SAP and its beneficial impact on the environment. Introduction Just as steam tackles the viscosity reduction of in situ oil in SAGD1,2 by heating it, solvents3,4,5,6,7 do this by dilution of the oil. Although employment of both steam and solvent together has also been discussed in the literature8,9,10,11,12,13,14,15, it has mostly focussed on enhancement of steam-flood or steam-stimulation. In their discussion on the subject Gupta et. Al16 described SAP as a process enhancement to SAGD where a small amount of a light alkane solvent VIZ propane, butane, pentane etc. or a mixture thereof is added to the injected steam. They also suggested with the help of lab experiments and numerical modeling that SAP has a potential to substantially improve the performance of SAGD. EXPECTED SAP ADVANTAGES Figure 1 shows a comparison, in a generic sense, of numerically obtained oil rate profile from a SAGD application vs. one obtained similarly with the application of SAP in the same reservoir. It is assumed that SAP would start after the expiry of a certain initial period in the life cycle of SAGD, to allow for the development of the chamber with steam. This comparison of the rate profiles suggests that the bulk of the oil that one would have produced in the later period with SAGD, can be produced sooner with SAP. The resulting acceleration of the production and the corresponding cash flow accelerations will lead to the improved economics of the project. Apart from the improved economics as a consequence of production rate acceleration, the other expected advantages of SAP include reduced environmental impact, possible down-hole upgrading of the heavy oil and small increase in the ultimate recovery. Since for given amount of recovered oil, expected steam to oil ratio in SAP is significantly lower than SAGD, corresponding reduction in the heat and fuel requirement lead to reduced impact on the environment.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it