Formation Pressure Testing while Drilling in Bohai Bay's Challenging Environment
Bibliographic record
Abstract
Abstract This paper describes the experience and lessons learned to acquire logging while drilling (LWD) formation pressure and near-wellbore mobility data in Bohai Bay. This area is known to be difficult in terms of measuring key parameters for reservoir description in conventional wireline logging (WL) programs. While comparisons between WL and LWD, including costs savings associated with the LWD approach, are common today in operators’ minds, the intangible benefits gained by real-time acquisition of these critical data are often neglected. Incorporating formation pressure testing into the drilling process, on the other hand, creates challenges to perform measurements in a timely manner as well as the need for continuous circulation while testing to ensure wellbore safety. Formation testing at Bohai Bay is difficult because of the unconsolidated formations and all aspects associated with this type of environment, such as borehole stability, hole washouts, sanding while testing, or lost seals. This paper describes successful test procedures, like the orientation of the probe into any direction, and discusses test examples from various hole sizes in detail. The key to achieve a high sealing success rate seems to be the ability to control and adjust the pad contact forces against the formation. Analyzing each drawdown sequence in the tool downhole and optimizing the drawdown rate and speed in the consecutive test reduces effects like sanding and improves the overall success rate. Providing this type of formation evaluation data with an LWD tool allows a continuous approach to data evaluation and decision-making. The ability to measure accurate LWD formation pressure data in a variety of hole sizes represents a significant opportunity for safe and cost-efficient wellbore construction, especially in environments like Bohai Bay.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".