Enhanced Tight Oil Recovery by Volume Fracturing in Chang 7 Reservoir: Experimental Study and Field Practice
Bibliographic record
Abstract
The Chang 7 reservoir in Changqing oilfield is rich in tight oil. However, due to the low formation permeability, it is very difficult to obtain economical oil production without stimulation treatments. Volume fracturing seems to be a more efficient tight oil recovery enhancement (EOR) method in Chang 7 pilot tests compared with conventional hydraulic fracturing. In this study, Chang 7 tight oil reservoir was first characterized by its geological property, hydrocarbon source rock distribution, and formation physiochemical property. Tight core flooding tests were then conducted to experimentally investigate the EOR ability of the volume fracturing technique. The field-scale practice was also demonstrated and analyzed. The results show that Chang 7 reservoir is favorable for the generation of a large amount of tight oil. Fractures created in tight cores can significantly improve the fluid flow conductivity and enhance the imbibition of displacing water, resulting in a greater tight oil recovery increment. Volume fracturing is an effective way to generate a larger number of fractures. Field application indicates that volume fracturing treatment can form a much greater reservoir stimulation volume. Daily oil production in the volume-fracturing-treated wells can be more than twice as high as that in the conventional-fracturing-treated wells.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".