Case Histories of Solvent Use in Thermal Recovery
Bibliographic record
Abstract
Abstract Steam injection is the most successful oil recovery method for heavy oil and oil sands exploitation. In some cases, solvents are added to steam when steam by itself fails to provide satisfactory recovery factors. In other instances, the use of solvents is justified on the basis of water usage reduction and decreasing greenhouse gas emissions. This has been done in cyclic steam stimulation (CSS), steamflooding, and steam-assisted gravity drainage (SAGD). The range of success in these instances is highly variable. Additional oil/bitumen recovery is claimed in most cases, but the commercial viability is often not discussed. The use of solvents for the recovery of viscous hydrocarbons is proposed for the obvious reason that mixing a solvent with a viscous oil reduces its viscosity, but the volumes needed are prohibitive; for example, 50% by weight of naphtha would be needed to reduce the viscosity of a one million centipoise bitumen to the level attained by a 200 °C (400 F) temperature increase. When solvents are used with steam, other phenomena are invoked to justify solvent use; for example, solvent condensation at steam temperature to promote the advance of solvent into cold heavy oil/bitumen ahead. In such postulates, the extremely low dispersivity of the solvent in the solid heavy oil/bitumen is of concern. The intent in these processes is to achieve a high mixing coefficient for solvent and heavy oil - just the opposite of what was sought in yesteryear's miscible displacement design. The feasibility of this intent is discussed, via a comparison of heat and solvent dispersion. Another line of thinking is that solvent diminishes the effect of formation heterogeneity. In this study, several case histories of solvent-addition thermal operations are discussed. A few non- thermal examples are also considered. Detailed results are not always available; the published information is tabulated. The results of the projects are discussed, and interpretations are made where possible. In recent years, solvent is being used in about 10% of the ~1500 SAGD well pairs in operation. In some cases, success is claimed. These are discussed in detail. Use of solvents in CSS is being done in Cold Lake; a new large project (Aspen) is planned there on the basis of solvent stimulation only. Solvents were used to a limited extent in some California steamfloods. None of those showed an improvement as a result of solvent addition. In a couple of cases, solvent injection was done unsuccessfully after a cold heavy oil production with sand (CHOPS) operation. This is a critical study of solvent use for the recovery of heavy oil/bitumen, showing that in most cases, solvents have not delivered the promise expected. In others, they totally failed. As such, it is hoped that the results will be of value in future planning of solvent injection.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".