Feasibility Of Environmentally Friendly Solvent in Bitumen Recovery Through Solvent-Steam Processes
Bibliographic record
Abstract
Steam flooding is the most widely used and reliable thermal enhanced oil recovery (EOR) process to recover bitumen. However, the excessive water required to generate steam causes environmental concerns. Thus, a noble idea to reduce the sole dependency on steam alone is to co-inject solvent with steam to improve miscibility aside from the oil displacement mechanisms from steam itself. However, the usage of industrial grade solvents are toxic, hence difficult to handle. These chemicals will cause long term health issues as well as environmental pollution. Thus, the aim of this research is to investigate the feasibility of a plant-based environmentally friendly solvent to replace the toxic industrial grade solvents as well as reduce the dependency on steam alone. \nEight core flooding experiments were conducted on a Canadian bitumen by varying propane, hexane, toluene, benzoyl peroxide, MS environmentally friendly solvent, and steam. Results obtained indicated good oil recoveries through solvent-steam processes especially using the MS solvent. The produced and residual oil were analyzed through asphaltenes separation which showed that the MS solvent produced more of the asphaltene in the produced oil. Meanwhile, the other solvents precipitated more asphaltenes in the residual oil on the spent rock since they are asphaltene precipitants except for toluene. \nPolar asphaltene should react with polar liquid water to form emulsion based on literature. However, the asphaltene content in produced oil did not correlate with the emulsion severity during investigation. Thus, control experiments involving individual SARA fractions were conducted to understand the role of each fraction in emulsion formation. Results showed that the mutual interaction between aromatics and resins induced the formation of emulsion before being stabilized by the asphaltenes. At the same time, it was found that emulsion formation intensifies during steam \n\ntemperature and exist as foam before condensing into more stable water droplets at a lower temperature. \nThese results meant that the MS environmentally friendly solvent could potentially replace the toxic chemicals during solvent-steam processes to mitigate the environmental footprint. Other than that, the role of asphaltene as an emulsion stabilizer instead of as an emulsion inducer will alter emulsion treatment and inhibition chemicals by targeting the deasphalted oil instead to improve the oil quality by removing the emulsion. This behavior of asphaltene will also shed light onto the fundamentals of asphaltene which still remains to be a complex molecule to understand.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".