Investigation of the Relation Between Coring Parameters and Formation Representation
Bibliographic record
Abstract
Abstract Coring is an essential operation for subsurface formation evaluation. It is a key to recover cores from formation and have them unaltered for best formation representation. Numerous reasons could influence core integrity that could produce data through core characterization that could misrepresent the formation. The aim of this paper is to investigate the influence of the (input vs. output) coring parameters and coring bit type on the coring performance and the cored formation property representation in high strength rocks. Such aim was investigated through coring operations applying Weight On Bit (WOB), rotary speed (revolution per minute - rpm), etc. In this research, high strength granite blocks were cored using a fully instrumented large-scale laboratory drilling simulator. Two main types of coring bits were utilized in the coring operations including a thick-wall coring bit and a thin-wall coring bit. The range of the applied WOB varied between a low weight (1kN) and a high weight (15kN) with increment increase of (2.5 kN). The applied rpm ranged between 60rpm and 300 rpm with increment increase of 60 rpm. Granite formation was characterized for rock property determination. The indirect Tensile (IT) Strength Test was conducted for the granite Material Characterization (MC). The analyzed and reported results of the overall coring performance, parameters (parameters vs. type of coring bit), and the IT for granite MC showed an influence of the un-optimized applied coring parameters and the random selection of the coring bit could remarkably impact the coring performance outcome and misrepresent rock property of the cored formation. Results recommend optimal selection of coring bit and applied coring parameters are essential for best coring performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".