Experimental Analysis of Heavy Oil Recovery and CO2 Storage by Alternate Injection of Steam and CO2 in Deep Naturally Fractured Reservoir
Bibliographic record
Abstract
Abstract Steam injection in heavy-oil containing naturally fractured reservoir aims at heating matrix to reduce the viscosity and enhance gravity drainage. This technique, however, is not feasible in deep reservoirs. Hydrocarbon solvent injection is also impractical due to low gravity of oil, heterogeneity, and retrieval of solvent diffused into matrix. A hybrid application of these two techniques was tested for deep reservoir conditions earlier (Steam Over Solvent Injection in Fractured Reservoirs Method -SOS-FR) and proved success if applied suitably. The cost of hydrocarbon solvent and greenhouse gas concerns, however, entail investigations on other techniques and materials. The use of CO2 in this type of process as solvent was considered and tested in this paper. Several issues are highly critical in this process. Like other hydrocarbon solvents used under non-isothermal conditions, the recovery process is highly sensitive to pressure and temperature as they determine the miscibility level. Also important is the capability of CO2 to extract matrix oil. Our earlier studies with light oil showed that heavier ends can be extracted if enough time is allowed for CO2 to interact with matrix oil. The same needs to be investigated for heavy-oils. Another dilemma was inverse proportionality of CO2 solubility with temperature. Steam (or heating) is inevitable to condition oil and decrease its viscosity before CO2 injection but temperature should be critically adjusted not to sacrifice CO2 solubility of oil. To clarify all these points and determine optimal application conditions (duration of each cycles and CO2 soaking time), we conducted a series of experiments by soaking core samples saturated with heavy oil into steam first followed by CO2. In the third cycle, steam (or hot water) was injected again to produce upgraded oil in the matrix. The experiments were performed under static conditions (soaking sandpacks and sandstone samples into steam or CO2 chambers) at different temperatures and pressures to determine optimal application conditions for mutual goals; heavy oil recovery and CO2 storage in the matrix. Finally, the results were compared to those of experiments with hydrocarbon solvents from technical point of view including storage benefits of CO2.
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 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".