Effective Extraction of a Heavy Oil Resource by an Environmentally Friendly Green Solvent: Limonene.
Bibliographic record
Abstract
Abstract Global oil consumption is predicted to increase by 15% from 2021 to 2050. The increasing oil demand and decreasing conventional oil supply force us to find alternate energy supplies. The key to this problem lies with the vast untapped heavy oil and bitumen resources. In this study, we investigate the effectiveness of an environmentally friendly solvent, limonene, in recovering heavy oil. Three core flood experiments representing three different recovery methods were carried out. These include steam flooding (E1), solvent flooding (E2), and solvent-steam co-injections (E3). The green solvent, limonene, is a citrus-based non-toxic solvent. It was chosen due to its high organic solvency and ready availability. Throughout the experiments, steam was injected at a cold water equivalent of 18 ml/min, while limonene was injected at 2 ml/min. The experiments were run with a back pressure of 45-55 psi. The core pack was prepared by filling the pore space of Ottawa sand with a 60% heavy oil sample and 40% water by volume (including water percentage in oil). Produced oil and water samples were collected every 20 min during the experiments. These samples were further analyzed by emulsion characterization to determine emulsion stability and oil quality. Spent rock analyses were done to calculate the displacement efficiency of each of the experiments. In addition, an economic analysis was done to determine the optimal recovery method. Spent rock analysis showed that a sole injection of limonene (E2) had the highest oil recovery. This confirms the high organic solvency of limonene achieved miscible flooding producing about 46 vol % from a total of 60 vol % initial oil. Steam flooding (E1), on the other hand, did not perform as well, producing around 29 vol %. The post-mortem sample from E1 indicated asphaltene precipitation which could have lowered oil recovery. Co-injection of limonene and steam was expected to yield the highest recovery due to the presence of two active drive mechanisms, thermal and miscible flooding. However, it performed comparatively less (41 vol %) than a sole injection of limonene (E2). This is further explained with emulsion characterization results. Experiments involving steam (E1 and E2) revealed strong emulsions in the oil produced, indicating a lower quality. Furthermore, it was seen that the solvent-steam process produced weaker emulsions compared to steam flooding alone. On the other hand, solvent flooding (E2) produced high-quality oil with little to no emulsions. These results along with the economic analysis, indicate that the optimal recovery method would be solvent flooding (E2). Our results prove that limonene is a promising organic solvent. Limonene is non-toxic, readily available, and safe to handle. As a result, it can be a safe green alternative to commonly used toxic organic solvents such as toluene.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".