Optimizing Hole Cleaning Using Low Viscosity Drilling Fluid
Bibliographic record
Abstract
When drilling for hydrocarbon, one most important thing to recognise is the bottom hole cleaning. Poor well hydraulics will lead to poor bottom hole cleaning. Several suggestions have been made in years back to prevent cuttings from falling to the lower side of the borehole thereby forming cutting bed. One of the main functions of drilling fluids is suspending the drill cuttings when the flow is static. But having met this criterion, cutting beds are still formed. The settling down of drill cutting makes this function of drilling fluid almost impossible. The formation of cutting bed due to the inability of the drilling fluid to establish this function brings about the objective of this research work. The main objective is to optimize hole cleaning using low viscosity drilling fluid and also to evaluate the effect of high flow rate on low viscous drilling fluid with respect to hole cleaning. This was carried out by a laboratory formulation of synthetic drilling fluid and the viscosity of this formulated fluid was varied from low to high. Tests for its rheological properties were carried out using Fann viscometer and the data obtained were recorded. The plastic viscosity and yield point were calculated from existing equations. The values for their rheological properties were tested using an existing hole cleaning model to determine the time taken for each of the drilling fluid to erode a 5 inches cutting bed. The fluid with an excellent hole cleaning value was also determined (CCI > or =1) and at optimum flow rate obtained for an 8-inches open hole section. When the values of their rheological properties were tested in the hole cleaning models, it was observed that, low viscosity fluids can erodes a 5 inches cutting bed height faster than the other drilling fluids and achieved an excellent hole cleaning value at an optimum flow rate when tested with the second model.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".