Study on Pressure Measuring and Formation Evaluation Methods while Underbalanced Drilling
Bibliographic record
Abstract
Abstract There are several instruments with which wellbore fluid pressure and flow parameters can be measured during underbalanced drilling(UBD). However, the real-time formation pressure test while drilling is yet a great challenge. Therefore, a much more precise pressure control drilling is not easy to be achieved. In this paper, technological methods and procedures are developed to obtain not only wellbore fluid pressure but also formation pressure while underbalanced drilling. Based on real-time pressure and fluids measuring during underbalanced drilling, methods to evaluate formation parameters such as permeability, porous media types and potential productivity are also developed. First, the pressure-while-drilling (PWD) methods, wellbore pressure calculation models and wellbore pressure rapid adjustment procedures are applied to get much more precise wellbore pressure profile and formation pressure while underbalanced drilling. Next, a surface real-time detection system is developed to detect the flux and fluid compositions while underbalanced drilling. Third, interpretation methods to evaluate formation parameters such as permeability, porous media types and potential productivity are also developed. These methods has been applied in three underbalanced drilling wells, one is a horizontal well and the others are vertical wells, which proved their significance at the first stage and reveal its potential in precise pressure control and formation parameters evaluation while underbalanced drilling.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".