Methods of Using Logs To Quantify Drillability
Bibliographic record
Abstract
Abstract Correlations between sonic logs and the formation drillability for different lithology types have been developed from data taken from 10 wells in North America. The gamma ray log was used in conjunction with drilling data to calculate the drillability. The drillability from penetration rate models is back calculated from bit design and reported field wear in conjunction with meter by meter operating parameters, formation type and pore pressure. Then this drillability was correlated with sonic logs for different lithologies as defined by the gamma ray log. The different formation types clearly show different correlations for the normalized correlations between drillability and sonic logs. The non-homogeneous lithologies are also correlated and normalized to rock strength from the sonic logs where the percent formation type mixtures are determined from the gamma ray. Data from multiple wells is presented showing the accuracy of the presented approach where more then 100,000 data points were statistically analyzed and evaluated in the development of the equations presented herein. The drillability from inverted penetration rate models has been verified to give good representation of rock strength based on comparison with triaxial laboratory data and makes the use of this model more versatile. The correlations provide improved estimations of rock strength which can be used in drilling performance simulation and wellbore stability studies.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".