Bibliographic record
Abstract
Abstract The Solvent Aided Process (SAP), described previously by the authors, is an improvement to SAGD that promises to enhance the economics of bitumen/heavy oil recovery projects and reduce water usage. In SAP, a small amount of hydrocarbon solvent (such as a low molecular weight alkane) is introduced as an additive to the injected steam during SAGD. The viscosity of the oil is reduced due to solvent dilution in addition to heating. SAP holds the promise of significantly improving the energy efficiency of SAGD, thus, reducing the heat requirement. Encana's field trials of SAP, discussed elsewhere, have shown the practical upside of this process. This paper discusses two conceptual optimizations of SAP. SAP reduces the effective steam-oil ratio (SOR) of SAGD. However, this comes at a cost as a part of the injected solvent is retained in the reservoir and lost in terms of economics. The smaller this cost is, the better are the economics of SAP. In this paper, the merits and drawbacks of using two alternate substances, SO2 and olefinated light alkanes, as solvents are discussed. Bottom-up SAP is a special geometrical configuration of SAP, discussed before. In this variant of the SAP process, bitumen recovery progresses from the bottom to the top of the reservoir, and employs injector and producer wells that are spaced horizontally apart rather than being in the same vertical plane. For large horizontal spacing between the injector and producer in a well pair, rates are low. On the other hand, for small spacing, the capital associated with the project is high. This paper explores the optimal horizontal spacing between the wells in a bottom-up SAP well pair. Introduction Steam-Assisted Gravity Drainage (SAGD) relies primarily on heat supplied to the reservoir by steam to reduce the viscosity of the oil(1). In SAP(2–5), solvent dilution is also taken advantage of to aid in this viscosity reduction. The result is an enhanced rate of oil production and recovery leading to superior economics with lower energy intensity and a reduction in water usage. To further improve the economics and energy efficiency of the process, in this paper, two optimizations of SAP are discussed; namely, employment of inexpensive solvents and optimal configuration of bottom-up SAP. SAP economics depend on the extent of the residual solvent in the reservoir(3) such that, the higher the inventory and cost of the unrecovered solvent, the less advantageous it is to carry out SAP. In principle, any of the commercially-available light alkanes (e.g. propane, butane or natural gas condensate) can be employed for SAP. However, these liquids tend to be expensive due to their multiple uses in the industry and their price generally varies in sync with light sweet crude. This fact naturally offers an opportunity to further improve the economics of SAP by looking for other solvents, and the discussion below describes two such alternative, inexpensive solvents.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".