Bibliographic record
Abstract
Design and development of optimal drilling-fluid systems, as well as their proper maintenance while drilling, are essential components of any successful drilling campaign. As the oil and gas industry is drilling in more-challenging areas (e.g., unconventional shale oil/gas wells, deepwater offshore wells, and deep high-pressure/high-temperature sour gas wells), the demand for more-accurate real-time assessment of the downhole state of the drilling fluids during drilling operations increases. Recent developments in drilling systems automation provide a multitude of opportunities to have real-time monitoring of drilling-fluid properties and early diagnosis of drilling-fluid-related complications that might arise while drilling. Coupled with closed-loop control of surface and downhole drilling-fluid properties, automated monitoring of fluid properties would allow rig personnel to make timely corrections to the drilling-fluid program, which eventually would lead to more-cost-efficient and safer drilling operations. This feature provides examples of such new technologies that can be used as part of the automated drilling-fluid monitoring system, allowing real-time control of drilling-fluid rheological properties (i.e., density and viscosity) and management of solids content with potential benefits of real-time management of equivalent circulating density, effective hole cleaning/cuttings transport, increasing drilling rate, and reducing nonproductive time, resulting in safer wells drilled at minimum costs. Recommended additional reading at OnePetro: www.onepetro.org. SPE 199101 - Field Results of a Real-Time Drilling-Fluid Monitoring System by Sérgio Magalhães, Universidade Federal Rural do Rio de Janeiro, et al. SPE 200990 - Intelligent Pressure-Control System for Managed-Pressure Drilling by Zhao Hui Song, Engineering Technology Research Institute of XDEC, et al. SPE 203389 - Real-Time Measurement of Drilling-Fluid Rheology and Density Using Acoustics by Paul Ofoche, Texas A&M University, et al.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.463 | 0.315 |
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".