Feasibility Study of the In-Situ Combustion in Shallow, Thin, and Multi-Layered Heavy Oil Reservoir
Bibliographic record
Abstract
Abstract All heavy oil reservoirs in TaoBao oil field are shallowly buried, thin, and multilayered in which natural production and cold production technologies have been implemented in past seven years, and obvious increase in oil production was achieved by cold production(up to 9 times than natural production method). However, due to the low reservoir pressure, heterogeneity, and limited formation thickness, the oil production has rapidly decreased, and more than half of the wells have been shut off now. At present, more than 94% oil remained in place is difficult to exploit, and different recovery methods are being invest-tigated, as a result, only in-situ combustion is more potential and seems feasible to enhance oil recovery for such reservoir. To investigate the feasibility of in-situ combustion, reactor experiment and combustion tube experiment have been carried out at first, the result shows that the optimal fire temperature is above 400°C, and the ultimate oil recovery is more than 80%. Additionally, air injection rate, combustion front velocity and other parameters have been measured or calculated. At the same time, primary reservoir numerical simulation for selected block in Bai92 reservoir are implemented considering that more importance should be attached to the feasibility of in-situ combustion in reservoir scale. Some important factors such as air injection rate are investigated. Moreover, the progress in pilot test of in-situ combustion in B92 reservoir is introduced briefly. The results of primary experiments, reservoir numerical simulation and pilot test show that in-situ combustion is feasible to enhance oil recovery of such shallow, thin and multilayered heavy oil reservoir as B92.
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 | 0.000 |
| 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) | 0.001 | 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".