Field Implementation of Solvent Aided Process
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 an 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 the implementation of a SAP pilot at Encana'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 has shown a corresponding decrease. 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 SAGD(1,2) by heating it, solvents(3–7) do this by diluting the oil. Although employment of both steam and solvent together has been discussed in the literature(8–15), these discussions have mostly focused on enhancement of steam-flood or steam-stimulation. In their discussion on the subject, Gupta et al.(16) described SAP as a process enhancement to SAGD where a small amount of a light alkane solvent, namely 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 modelling, that SAP has the potential to substantially improve the performance of SAGD. Expected SAP Advantages Figure 1 shows a comparison of an expected 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 initial development of the chamber with steam. The units from the rate and time axes are omitted to emphasize the general nature of these profiles. The comparison of the rate profiles is provided in order to suggest that the bulk of the oil that would have been produced in the later period with SAGD can be produced sooner with SAP. The acceleration of production and the corresponding cash flow could lead to improved economics for 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 a small increase in the ultimate recovery.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".