Effect of Solvent Sequencing and Other Enhancements on Solvent Aided Process
Bibliographic record
Abstract
Abstract Alberta 's oil sands clearly present an economical solution to the world 's dwindling conventional oil supply. Over 80% of these oil sands can only be recovered by in situ methods, such as SAGD technology. However SAGD, in its current commercial form, is an energy intensive process. The authors have previously described an improvement to SAGD —Solvent Aided Process (SAP) —that aims to combine the benefits of using steam with solvents. In SAP, a small amount of hydrocarbon solvent is introduced as an additive to the injected steam during SAGD. SAP holds the promise to significantly improve the energy efficiency of SAGD, thus reducing the heat requirement. Previously discussed results from EnCana 's field trials of SAP have shown the practical upside of this process. In theory, a variety of solvents can be employed with steam to tap the benefit of solvent dilution in combination with (in-situ) heating the oil. However, due to their commercial availability, light alkanes present in the natural gas condensate are the practical choice for this purpose. These different solvents can be used individually or as a mixture, together or sequentially, with varying degrees of benefit. The economics of a SAP project depends on the enhancement of oil recovery and rates as well as solvent recovery. This paper, based on modeling work, investigates the effect of solvent sequencing, the impact of cross-flow of gas on solvent recovery, the effect of low pressure operation on SAP performance and the operation of SAP in bottom-up geometry. Based on this work, the performance of SAP can be improved by employing proper sequence of solvent, cross-flow solvent recovery and low pressure operation. Bottom-up SAP takes the bitumen recovery to new levels of energy efficiency with steam-oil ratios below 0.5. Introduction As previously described, in SAGD, oil viscosity is reduced by heating with steam(1,2). In SAP(3–5), solvent dilution is also taken advantage of to aid this viscosity reduction. The result is an enhanced rate of oil production and recovery leading to superior economics with lower energy intensity and impact on the environment. VAPEX, a process similar to SAGD, employs only hydrocarbon vapour instead of steam as described in the literature(6–9) and can eliminate the expensive heating requirement of SAGD. However, its development is awaiting a successful field trial. Use of solvent with steam for oil recovery is also discussed in the literature(10–13) with a focus on the enhancement of steam displacement or steam stimulation. Using solvent with steam in a gravity drainage context offers some practical advantages. The pressure in the vapour chamber does not need to be supported by a non-condensable gas as required in some embodiments of VAPEX. This means that the progression of the vapour chamber in SAP does not get overwhelmed by the heat/mass transfer resistance at the vapour/oil interface. EnCana has been developing SAP since 1996 and piloted the process first at its Senlac Thermal Project in 2002. Encouraged by the results, it is presently testing SAP for in situ bitumen extraction at its Christina Lake Thermal Project(5,14).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".