Heavy Oil and Bitumen Recovery by Hot Solvent Injection: An Experimental and Computational Investigation
Bibliographic record
Abstract
Abstract Thermal and miscible methods are commonly used for in-situ recovery of heavy oil and bitumen. Both techniques have their own limitations and associated shortcomings, often times yielding an inefficient process. The most common thermal method is steam injection, which is highly energy intensive. Steam generation costs and water production affect the economics of the thermal technique adversely. On the other hand, miscible methods are energy effective but their economics depends on the solvent retrieval. Various combinations of these two techniques such as co- or alternate injection of steam and solvent have been proposed as a solution, but no optimum method has yet been developed. Thermal and miscible methods can be combined by co-injecting solvent with steam or injecting solvent into a pre-heated reservoir. Current work was undertaken to study the performance of solvents at higher temperatures for heavy oil/bitumen recovery. Glass bead packs and Berea sandstone cores were used in the experiments to represent different types of pore structures, porosity and permeability. After saturating with heavy oil, the samples were exposed to the vapor of paraffinic solvents (propane and butane) at a temperature above the boiling point of the solvent, and a constant pressure of 1500 kPa. A mechanical convection oven was used to maintain constant temperature across the setup. The setup was designed in such a way that a reasonably long sample (up to 30 cm) can be tested to analyze the gravity effect. The oil recovered from each of these experiments was collected using a specifically designed collection system and analyzed for composition, viscosity and asphaltene content. The amount of oil recovered in each case was also analyzed and the quantity and nature of asphaltene precipitated with each of the tested solvents under the prevailing temperature and pressure of the experiment was reported. It was observed that recovery decreased with increasing temperature and pressure of the system for both the solvents, and that the best results were found when experimental temperature is only slightly higher than the saturation temperature. It was also noticed that butane diluted the oil more than propane which resulted in lower asphaltene content and viscosity of oil produced with butane as a solvent. To understand the mechanics of the process and to explain the observations of these experiments, additional experiments were done on tighter samples of different sizes, along with numerical simulation and visualization experiments. The effects of temperature and pressure on the recovery were studied using a commercial reservoir simulator. Propane, butane and pentane were used as solvents. Asphaltene precipitation was also modeled. To investigate the phenomenon further, 2-D glass beads packed models and Hele-Shaw cells were constructed, saturated with heavy-oil, and exposed to different types of solvents. The setup was continuously monitored to observe fluid fronts and asphaltene precipitation. The images were used in history matching to numerical models by mainly considering the permeability reduction due to asphaltene precipitation and the extent of interaction between solvent and oil phase. Using this analysis, the mechanics of the process was clarified from the effect of solvent type on the recovery process point of view. The optimum solvent concentration and optimum operating temperature for hot solvent process were also identified.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".