Research and Practice on Gas Channeling Controlling by Combined Stimulation for Multi-Thermal Fluids Huff and Puff
Bibliographic record
Abstract
Cycle steam stimulation has been widely used in land oil field. In order to get higher oil recovery rate and higher cumulative oil production, one improvement work of cyclic steam stimulation is injection N2 and CO2 together with steam to enlarge the heated radius, and the pilot test of multi-thermal fluids huff and puff were carried out in offshore heavy oil in Bohai. There were 10 horizontal wells which have accomplished the 1st cycle stimulation, and 6 wells have accomplished the 2nd cycle stimulation. There were 4 well times of gas channeling in the 1st cycle injection, and 9 well times in the 2nd cycle injection. Because of the formation pressure drop, gas channeling became more serious with the increase of stimulation cycles. Based on the well performance, the reason and characteristic of gas channeling for injection multi-thermal fluids were analyzed, which is different from that of steam injection. Based on a numerical model, a quantitative researched about the influence of gas channel to thermal well was carried out. In order to manage the gas channeling, a combined stimulation program was purposed. Case study of gas channeling controlling by combined stimulation in N heavy oil field in Bohai shows that, the combined stimulation can relieve the risk of gas channeling. The new multi-thermal fluids injection model is of great significance for thermal recovery of offshore heavy oil.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".