Reducing Consumed Energy while Drilling an Oil Well through a Deep Rig Time Analysis
Why this work is in the frame
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Bibliographic record
Abstract
As time goes by, increase in world energy demand forces oil and gas companies to drill deeper in order to produce more oil and gas for balancing world’s offer and demand. This requires drilling layers with various characteristics and dealing with more drilling problems as drilling progresses. Reduction of drilling problems can help drillers to reduce their cost effectively. Rig time break down of more than 300 wells in one south west Iranian oil field has been analysed to determine effective parameters on non-productive time amount. Results show that the most common drilling problems always have been experienced by drilling engineers are Equipment failure, stuck pipe and lost circulation which expose huge expenses to the oil companies. Several factors while drilling will govern how severe mud loss and stuck pipe would occur. These actually make analytical modelling of lost circulation or pipe sticking to somehow complicated. Hereby, employing artificial intelligence can be a leeway with proven capability and accuracy. In this research, operational parameters in Maroun oilfields are introduced to artificial neural networks to predict lost circulation severity, stuck pipe position and stuck pipe severity before happening. Results are well-matched with reality. Key words: Energy; Drilling problems; Lost circulation; Stuck pipe; Rig time analysis
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 it