Mitigation of Slim Open Hole Wireline Logging Risk Under High Overbalance Conditions
Bibliographic record
Abstract
Abstract The oil and gas fields offshore Malaysia-Thailand border are well known for the presence of high pressure and high temperature reservoir characteristics. The hot environment favours the use of hostile wireline logging tools over logging while drilling tools for some measurements. High overbalance conditions are sometimes a necessary requirement to safely drill particular formations but bring with them operational challenges. For wireline logging a critical hazard is that of becoming differentially stuck, especially in the case of stationary logs such as pressure measurements, sampling, and coring. A reduction in allowable safe stationary times for these measurements can also compromise the quality of the results. Production wells in this area are generally drilled with slim 6-1/8″ sections through the reservoir. As the borehole becomes smaller the risk of sticking increases as the tool contact area increases and so is a particular concern in slim holes. This often leads to logging being run using costly pipe conveyed methods or the inability to obtain required data altogether. This paper presents case histories of wireline formation pressure testing and sampling in 6-1/8″ sections with high deviation, 3D trajectory boreholes with overbalance pressures of over 2,000 psi. The use of a high-performance roller system to create tool stand-off to mitigate differential sticking as well as eliminate friction is described, along with the use of an engineered hole finder to address hole access concerns. The importance of wireline simulation models to identify risks and test solutions in such conditions is also considered. Slim hole logging and logging under overbalanced conditions are often a source of concern for subsurface, drilling and service company personnel alike. Describing a successful approach to address both is of interest to all involved parties and should provide ideas and confidence to plan cost-effective logging in similar 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".