Evaluation and scientific justification of polymer flooding application in the Uzen oilfield
Bibliographic record
Abstract
The Uzen field is at the late stage of development, with an average field water cut of over 90% due to a long-term non-shale waterflooding. At the same time, the current oil saturation of the reservoir is estimated as more than 50%, which may provide a promising future for the field. According to world practice, the application of chemical enhanced oil recovery (CEOR) methods can provide high oil recovery, and extend the cost-effective life of the field. Therefore, the application of chemical enhanced oil recovery is particularly relevant considering the current status of the Uzen field. The main method of CEOR is polymer flooding (PF), which has proven its high efficiency over 60 years of application in the industry. The objective of this study is to evaluate and justify the application of the polymer flooding technology to the conditions of the Uzen field. According to this objective, first of all, the world experience of polymer flooding was studied, including the latest large-scale projects in the fields of China, the USA, Canada, India, Oman and other countries. Criteria for the effective application of polymer flooding were developed and screening of the geological and physical characteristics (GPC) of all objects of the field was carried out. In addition, facies maps and the current state of development were analyzed to select a potential pilot test site. A review of the surface facilities, an analysis of the water supply sources and the characteristics of different polymers were carried out to develop a project for the pilot test of the technology. Preliminary hydrodynamic calculations show reduced water cut, increased oil production and an increase of 7% in oil recovery. Feasibility studies prove the profitability of the polymer flooding at oil prices above $55/bbl. This study is the basis for further implementation, adaptation and optimization of polymer flooding
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".