Thermal Performance Challenges and Prospectives of the Russian Largest Carbonate Reservoir with Heavy Oil
Bibliographic record
Abstract
Abstract In carbonate reservoirs with heavy oil, the implementation of "classical" thermal EOR methods such as cyclic steam stimulations, steamflooding, and steam assisted gravity drainage usually demonstrates a lower efficiency in comparison with sandstones. The key performance problems are a very complicated porous structure of carbonates represented by matrix blocks, fractures, and vuggs, and a negative wettability of carbonate rocks, which remains mostly oil-wet even with heating. These suggestions are fully confirmed by actual and laboratory results of the Permian - Carboniferous reservoir of the Usinsk field located in Northwest European Russia. The reservoir has the largest heavy oil remaining reserves in the carbonate reservoirs of Russia. Since the viscosity of its oil is more than 700 mPa*s, in some areas of the reservoir, there is a steam injection at ~300°C and ~10 MPa, which are being used for 25 years via vertical and horizontal wells. However, the current oil recovery numbers of the pilots are estimated only between 12 and 15 %. This work includes the analysis the results of recently conducted experimental studies with stacked core models and the actual production and injection data of the thermal pilots with different well configurations.
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 | 0.000 |
| 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".