Optimization of Multiple Bit Runs Based on ROP Models and Cost Equation: A New Methodology Applied for One of the Persian Gulf Carbonate Fields
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Improving the rate of penetration (ROP) is one of the key methods to reduce drilling costs. Several ROP models have been developed and modified based on the concept where unconfined compressive strength (UCS) is inversionally proportional with the rate of penetration. These models can predict the rate of penetration of different bit types in an oil or gas field with a reasonable degree of accuracy. The ROP model studied herein relates the rate of penetration to operating conditions and bit parameters in addition to the rock strength. Also, the effects of bit hydraulics and bit wear on rate of penetration are included in the model. In this paper, the drilling performance was optimized, using the ROP models, for upcoming wells in one of the Persian Gulf carbonate fields. Based on previous drilled wells a rock strength log along the wellbore is created and modified to mach the the new well survey. The rock strength is back calculated from the ROP model which includes bit design and reported field wear in conjunction with meter by meter operating parameters, formation lithologies and pore pressure. By conducting a number of simulations a learning curve was constructed to obtain the optimum bit hydraulics, best combination of operational parameters and the most effective bit design. Based on the proposed ROP model, a simple and useful simulator was developed. This methodology can be used in pre-planning and post analysis to reduce drilling cost where previously drilled wells exist.
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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 | 0.000 |
| 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