Increased Drilling Efficiency of Gas-Storage Wells Proven Using Drilling Simulator
Bibliographic record
Abstract
Abstract Gas storage wells are being drilled in Nova Scotia, Canada for natural gas storage in underground rock and salt caverns. A study was performed to evaluate the efficiency of the drilling of these wells. Utilizing a drilling simulator where drilling operational or log data is required to generate a drillability log or apparent rock strength log (ARSL) offset gas storage wells in the area were analyzed. The sonic log data from the offset wells were utilized to calculate the ARSL and the formation information was obtained from strip logs. The actual drilling of the next gas storage well was then simulated and optimized using the commercially available drilling simulator. The optimization process proved that the potential for reducing drilling cost is more the 30 percent. This is a typical cost reduction that can be obtained utilizing drilling simulation on conventional wells in other areas of Canada were only a few offset wells have been drilled. The analysis proves that even with unconventional drilling like for gas storage well with larger hole sizes and the utilization of hole openers the cost reduction potential is the same as with conventional drilling. This paper presents the field data utilized and the results from the simulation study including the economical analysis.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".