Selection of Thermo-Chemical EOR for a Large Carbonate Reservoir with Heavy Oil Based on Results of Laboratory Experiments
Bibliographic record
Abstract
Abstract This work summarizes the results obtained during the laboratory tests carried out to choose the most efficient thermo-chemical EOR for the further development of the Permian - Carboniferous reservoir of the Usinsk field which is the largest object with heavy oil (its viscosity is around 710 mPa*s) in the Timan-Pechora oil and gas region located in the North European part of Russia. The laboratory experiments were conducted using the stacked models of full-sized and standard-sized core samples of carbonate rocks of the reservoir. Due to the oil-wet characteristic of the carbonate formations, the large volume model was initially imbibed by oil without modeling the water connate saturation. The results of these studies suggest that the combined use of hot water with a temperature of 250°C and a NOP surfactant increases the oil recovery factor up to 38 %. The reason of this relatively low efficiency of the considered thermo-chemical EOR is connected to saving the hydrophobic characteristic of the carbonate formations even upon their heating above 200°C.
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 | 0.001 |
| 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) | 0.001 | 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".