Drilling-Derived Rock Strength Can Simulate Upcoming Well Performance
Bibliographic record
Abstract
Abstract A drilling simulator has been used during the past four years to improve the drilling performance in Western Canada. Rate of penetration improvement and subsequent cost and time reductions are the key elements for drilling these wells. A drilling simulator is required to generate the "Apparent Rock Strength Log" (ARSL) using available offset well data. The ARSL calculation is based on using inverted Rate of Penetration (ROP) models for different bit types, reported bit wear, lithological information and pore pressure in addition to the drilling parameters. The generated ARSL can be modified and correlated for different and new formation tops for planned wells. The obtained ARSL logs for the wells in the same field have shown an acceptable overlay for the common lithologies using different bit runs and drilling parameters. Furthermore, it can be shown that the ROP match with the new simulated ROP in the same well applying another well's drilling parameters once their ARSL are adjusted. It has been shown that a typical cost reduction can be achieved for the planned wells utilizing the drilling simulator when previously drilled wells exist. The effect of using the combination of different bit runs and drilling parameters can also be explored through use of the simulator. In this paper, a study was conducted for two wells in an Albertan, Canada field to investigate the effect of using the bits used in a well to reduce cost and optimize the next well. The ARSL logs of two wells were separately obtained and compared. The comparison between the new simulated ROP for the first well, using another well's drilling data, and the available ROP for the second well is also discussed. Final results are showing an acceptable match obtained for ROP values as well as for the corresponding drilling time and final bit wear status in each of the bit run sections. Utilizing the simulator in these type wells shows a significant cost and time reduction potential and can be helpful to apply in preplanning analysis for new wells to be drilled using previously utilized bit types and designs.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".