Optimization of Tertiary Water-Alternate-CO2 Flood in Jilin Oil Field of China: Laboratory and Simulation Studies
Bibliographic record
Abstract
Abstract Most of the low permeability oil reservoirs in Jilin oil field of China have reached their economic limit of production by waterflooding and even many wells have been abandoned due to low productivity. Interest in recovery enhanced technology of tertiary miscible or immiscible CO2 flooding is increasing in these low permeable reservoirs. In this paper, a laboratory study using a high-pressure PVT cell and a simulation study using full-field fully equation-of-state (EOS) compositional reservoir modeling were undertaken to optimize the design of a miscible or immiscible CO2 flood pilot project for the Xinli Unit in Jilin oil field. The laboratory study includes phase behavior analysis, asphaltene deposition assessment, and minimum miscibility pressure (MMP) determination in the CO2 corefloods. Based on building a full-field 3D geologic model and history matching waterflood performance, a preliminary CO2 flood reservoir modeling has been used to distinguish displacement mechanisms and reservoir performance of natural depletion, continued waterflooding, continuous CO2 and water-alternate-CO2. The simulation study and the pilot test showed water-alternate-CO2 after waterflooding is an effective method of improved oil recovery for the low permeability reservoir and it can appreciably reduce water production and enhance oil recovery. Simulation studies has also been completed to determine an optimal water-CO2 ratio, optimal CO2 slugs and optimal CO2 injection rate. The pilot operation is now well implementing according to above-mentioned study achievements. Future plans for water-alternate-CO2 optimization include continuation of performance monitoring to help optimize tapering strategy in order to enhance further oil recovery in the low permeability oil reservoir.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".