Miscible flooding Performance of Terpenes as Green Solvents for Heavy Oil Recovery
Bibliographic record
Abstract
The vast reserves of heavy oil present a possible solution to the world’s energy demands, but the challenge of low mobility due to high viscosity hinders their extraction. There are two methods that can be used to enhance their extraction. The methods are heat introduction and chemical injection. Steam injection is the most reliable heat introduction method but its usage of large amounts of fresh water, emission of significant amounts of carbon dioxide, and heat losses makes its usage a major drawback. \nOn the other hand, solvents are the most effective chemicals to reduce viscosity and enhance mobility, but are costly for use in the field, toxic and difficult to handle and transport to remote locations. Thus, this research investigates the feasibility and effectiveness of environmentally friendly solvents. \nFive core flooding experiments were conducted on a heavy oil sample. 16.49 API gravity and 140,000 cP viscosity heavy oil sample was blended at 60% initial oil saturation with 39% porosity Ottawa sand. 40% of the pore volume was filled with distilled water. Performance of four environmentally friendly solvents (d-limonene, turpentine, citronella and beta-pinene) was compared with toluene on this heavy oil. All solvents were injected at 2 mL/min flow rate. Produced oil qualities were further examined in terms of water content (water-in-oil emulsions) by using an optimal microscope. Later, the amount of solvent and water in produced oil samples were determined through Thermogravimetry Analysis / Differential Scanning Calorimetry (TGA/DSC) analysis. Compositional analysis on produced oil samples was carried out through Saturates, Aromatics, Resins, Asphaltene (SARA) fractionation. To better understand the performance differences in each solvent use, viscosity measurements were conducted on solvent-heavy oil blends at varying solvent doses. Finally, an economic analysis was conducted on each solvent to understand their feasibility. \nIn all experiments, high oil recovery was obtained because all solvents used in this study were miscible with the heavy oil used in this study. Hence, first-contact miscibility was maintained in all experiments. Toluene yielded the highest oil recovery with the highest quality due to its greatest solvency power among all solvents used in this study. However, due to its toxicity, most oil companies in the United States will not implement toluene for use in their oil recovery projects. D-limonene and beta-pinene solvents gave comparable results with toluene, with only 5% lower recovery but less environmental impact. SARA analysis shows that, citronella resulted in the least asphaltene content and greatest aromatic content. This proves that citronella resulted in the best quality of oil produced. Viscosity measurements on varying solvent-heavy oil concentration shows that even at lower concentration of each environmentally friendly solvent, similar mobility enhancement was achieved on heavy oil when compared to toluene-heavy oil blends. This is because of the molecular structures of terpenes. These structures are known as degreasers and reduces the crude oil viscosity significantly. \nThis study shows that terpenes are effective and feasible green solvents that can be used for heavy oil recovery. Because this study is the first study conducted on terpenes, it brings new insight to heavy oil production with an environmentally friendly way.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".