Experimental Investigation of CSS Assisted by Gas-viscosity Reducer Co-Injection with Different Types of Wells and Heavy Oil
Bibliographic record
Abstract
The efficiency of conventional thermal recovery methods is limited due to heat loss, steam overlapping and other serious problems. Steam injection assisted by various additives, such as no-condensable gas, solvent and surfactant, has proved to be an effective and beneficial method to improve thermal oil recovery. However, based on literature review, few systematic and comprehensive explanation of the mechanism of the compound system of gas-chemical agent and the application criteria. In this paper, 3D physical experiments with different types of wells and heavy oil were conducted. The additives consist of nitrogen and viscosity reducer (VR). Different injection fluid combinations (single gas, single VR and gas-VR co-injection), fluid injection configurations (gas-steam and gas+steam, VR-steam and VR+steam,) were designed to study the effects of the compound system on oil recovery, oil-steam ratio and oil production rate. The results indicated that steam injection assisted by gas-VR performs well in enhancing the thermal recovery. Some conclusions are drawn according to the variation curves of characteristic parameters. The effects of the compound system still worked and increased the oil recovery after different injection patterns. Meanwhile, the cumulative SOR decreased to the different extent after the corresponding processes sequentially. The distribution of temperature showed that gas-VR co-injection not only inhibited steam overlapping, which promoted the horizontal expansion of the steam chamber but also reduced the viscosity of heavy oil significantly. More oil was produced due to the gas expansion energy. In summary, this work provides a practical understanding of CSS assisted by gas-VR co-injection and optimizing of the injection schemes for different types of reservoirs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".