Bibliographic record
Abstract
Abstract Confined Compressive Strength (CCS) offers a more accurate indication of rock hardness and rock formation drillability, which is useful in optimizing the drilling operation. PhiDrillSim is a machine learning app that predicts the CCS using neural network techniques. The goal is to predict the Confined compressive Strength using the Neural network technique based on the operation dataset, which will be used to train the neural network model. A specific operation dataset will be provided for the training of the model in the app. It will predict the CCS model of the rock formation and compare it with the actual CCS collected. Preliminary results show that the accuracy of the predicted CCS values is consistent with the CCS calculations using the log data method. There are various methods of calculating the CCS of the formation, with varying degrees of cost and difficulty. Furthermore, the accuracy of CCS measurements directly affects the drilling cost of operations, affecting drill-string durability and operational time. This paper explores the accuracy of the PhiDrillSim App in comparison with a log data method in calculating and predicting the formation of CCS. CCS Description Rock strength is essential in the drilling process. Confined Compressive Strength (CCS) is a geomechanical rock property that indicates the rock strength when confined to some medium. (Fabian, 1994). Since UCS is widely used, the classification of rock formations based on UCS is readily available. Moreover, the estimation of rock strength classification is based on UCS. CCS then will be related to UCS using the equation proposed by Caicedo et al., 2005.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".