Study and Application of New Technology to Increase Drilling Speed of Ultra-Deep Well in Yuanba Area
Bibliographic record
Abstract
The geologic condition in Yuanba Area is quite complex.The drilling problems of formation leakage, pressure differential sticking, narrow density windows and other issues are more and more prominent. Drilling efficiency is low with long drilling cycle because of abnormal complex engineering geological characteristics such as thick continental formation, interbeded sand shale, poor drillability, ultra-high pressure in J 1 z and T 3 x formation, narrow pressure window. 12 completed wells in Yuanba area are analyzed, the conclusion can be draw that improving drilling efficiency in Yuanba region is quite potential if complexity underlying can be decreased and ROP can be improved. In view of this, the matching drilling technologies and tools are introduced and applied, the result show that optimization technology of casing program, bit optimization, gas drilling technology, compound drilling technology and corresponding new tools has made great success in Yuanba area, the average ROP was increased by 20.25%, drilling period was shortened by 18.33%, and the average complex accident handling time was reduced by 25.40%, which provides a good reference for ultra-deep well drilling. Key words : Ultra-deep well; Gas drilling; Bit optimization; Compound drilling; Drilling ROP
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".