Experimental Study of Cuttings Transport with Non-Newtonian Fluid in an Inclined Well Using Visualization and Electrical Resistance Tomography Techniques
Bibliographic record
Abstract
Summary Hole cleaning is a concern in directional and horizontal well drilling operations where drill cuttings tend to settle in the lower annulus section. Laboratory-scale experiments were performed with different non-Newtonian fluids in a 6.16-m-long, 114.3- × 63.5-mm transparent annulus test section to investigate cuttings transport behavior. This experimental study focused on understanding the cuttings transport mechanism in the annulus section with high-speed imaging technology. The movement of cuttings in the inclined annular section was captured with a high-speed camera at 2,000 frames/sec. Also, cuttings bed movement patterns at different fluid velocities and inner pipe rotations were captured with a digital single-lens reflex video camera. The electrical resistance tomography (ERT) system was used to quantify the cuttings volume fraction in the annulus. Different solid bed heights and cuttings movements were observed based on fluid rheology, fluid velocity, and inner pipe rotation. The mechanistic three-layer cuttings transport model was visualized with the experimental procedure. This study showed that solid bed height is significantly reduced with an increase in the inner pipe rotation. This study also identified that cuttings bed thickness largely depends on fluid rheology and wellbore inclination. The image from the high-speed camera identified a downward trend of some rolling particles in the annulus caused by gravitational force at a low mud velocity. Visual observation from a high-speed camera identified a helical motion of solid particles when the drillpipe is in contact with solid particles and rotating at a higher rev/min. Different cuttings movement patterns such as: rolling, sliding, suspension, helical movement, and downward movement were identified from the visualization of a high-speed camera.
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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 | 0.000 | 0.000 |
| 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".