Optimization of Drilling Operational Parameters and UBD Feasibility Study in a Mature UAE Field Wells
Bibliographic record
Abstract
Abstract Mechanical efficiency concept derived from the specific energy theory was used for a comprehensive drilling performance analysis. Data from 17 wells drilled in a mature UAE field were evaluated. A wellbore stability analysis of the wells was also conducted by using linear elastic theory of rock deformation. Potential application of the UBD techniques in has been evaluated, based on the results of the wellbore stability analysis. Results have shown that: The mechanical efficiency concept is applicable for the evaluation and improvement of the drilling performance of the wells drilled in the field under consideration.Based on the drilling Mechanical Efficiency concept, the optimum operational parameters such as weight on bit, rotations per minute, bit type, and bit hydraulic horsepower could be identified.The operational window between formation pore pressure and minimum drilling fluid pressure to avoid borehole collapse was found to be too narrow both in vertical and horizontal wells.Results of the wellbore stability analysis indicate that there will be a risk of borehole collapse if UBD techniques are used. Therefore, it is recommended to carry out more in depth analysis of in-situ stresses before making any decision on the use of UBD techniques for drilling vertical and horizontal wells in this mature UAE field.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".