Development and characterization of synthetic rock-like materials for drilling and geomechanics experiments
Bibliographic record
Abstract
The Drilling Technology Laboratory (DTL) have been investigating relationships between vibrations and Rate of Penetration (ROP) using real sedimentary rocks and synthetic rocks for years. However, identical sedimentary rock samples are not easy to obtain in local area (onshore area of eastern Newfoundland). Therefore, this research is focusing on developing synthetic rocks as a substitute for real sedimentary rocks and characterizing petroleum related physical properties of synthetic rocks. These Rock-Like Materials (RLM) are essentially fine grained concretes based on the use of Portland Cement, fine aggregate, water and related admixtures which can meet the research requirements. A new approach has been proposed by Prasad (2009) [1] to describe drillability of rocks in a quantitative way with eight parameters which include density, porosity, compressional and shear wave velocities, unconfined compressive strength, Mohr friction angle, mineralogy and grain size. this method is adopted and modified in this research to be suitable for synthetic rocks developed previously. The eight parameters tests are conducted in the drilling technology lab to characterize the properties of the synthetic rocks and to provide the basis for the future work. In this research, standard procedures of making concrete (synthetic rocks) and standard procedures of eight parameters tests have been established with quality assurance.
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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 | 0.001 |
| 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".