Effect of Near Wall Turbulence on the Particle Removal From Bed Deposits in Horizontal Wells
Bibliographic record
Abstract
Although solids entrainment and deposition mechanisms have been studied extensively over the years, our understanding of fluids-particle interactions near bed interface is still limited. Progress toward such understanding has been relatively slow because of the difficulties inherent simultaneous measurement of local solids transport and adjacent near-bed fluid flow. With the introduction of non-intrusive measurement techniques such as Particle Image Velocimetry (PIV), it is now possible to determine the instantaneous velocity field and observe particle deposition/resuspension simultaneously under non-uniform flow conditions. An experimental program was conducted to investigate different aspects of turbulent flow of water over the cuttings bed deposited in horizontal annuli. A large-scale horizontal flow loop consisting of 9 m long high quality optic glass pipes (95 mm ID of outer pipe and 38 mm OD of inner pipe) equipped with state of the art PIV system has been used for the experiments. Turbulent flow over cuttings bed experiments were conducted at superficial Reynolds numbers of 9300 and 10800. Natural irregular shaped quartz sands with 3 different mean sieve sizes of 260, 350 and 600 micron were used as solid particles. The proposed work was accomplished through several tasks: i-) conduct experiments to measure the instantaneous local velocity profile during turbulent flow in the horizontal concentric annuli and examine the effect of stationary cutting bed on the local velocity profile, Reynolds stress and turbulence intensity; ii-) investigate the impact of particle size on the near-wall turbulent activities. Results have indicated that existence of a cuttings bed on the lower side of the wellbore dramatically alters the near wall velocity profile comparing to the case with no cuttings bed. Presence of cuttings bed causes the maximum velocity to shift toward the inner pipe. Presence of stationary cutting bed causes a reduction in Reynolds stress, axial and radial turbulence intensity, which in turn, would adversely affect the hole cleaning. Larger cuttings slightly enhanced turbulent stress and radial intensities. However, the increase in these entities as a result of increasing cutting size was far less than the decrease in them as a consequence of presence of stationary cutting bed. Axial turbulence intensity was the same in the core flow away from the cuttings bed for flow with and without a cuttings bed. However, the peak of axial intensities is shown to be less for flow near the cuttings bed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".