Hot Solvent Injection for Heavy Oil/Bitumen Recovery from Fractured Reservoirs: An Experimental Approach To Determine Optimal Application Conditions
Bibliographic record
Abstract
We conducted a series of dynamic experiments in which liquid solvent (heptane) was injected into heavy-oil-saturated artificially fractured Berea sandstone samples with and without prethermal injection. To account for the effect of wettability on the process, experiments were repeated on the samples exposed to a wettability alteration (more oil-wet) process. Cores were saturated with heavy crude oil and placed inside a rubber sleeve. Next, the system was placed into an oven and maintained under constant temperature conditions. Then, either hot solvent (superheated to be in the vapor phase) or cold solvent was introduced into the system through the fracture at a constant rate. Pressure and temperature was continuously monitored at the inlet and center of the core. Properties of the oil and liquid condensate from the gas produced were measured and analyzed. This scheme was repeated for a wide range of temperature conditions. The retrieval of the solvent during the solvent injection phase and post-thermal method (steam or hot-water) injection performed for a wide range of temperature was monitored. Our results and observations indicate that the first requirement for a successful application is an effective solvent diffusion into matrix before it breaks through and improves the gravity drainage of oil by dilution. The second requirement is solvent retrieval. We also observed that a critical temperature and injection rate exists that yields a maximized oil recovery and solvent retrieval.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".