Application of Ditch Magnet System to Extend Downhole Tool Life and Accuracy – Revealing the Quantity of Metal Contamination in Drilling Fluid
Bibliographic record
Abstract
Abstract Magnetic contamination of drilling fluids is assessed. It is shown from experience how much material is typically removed from current drilling operations. Known sources for these materials are outlined, as well as the effect these materials have on down hole tools and measurements. To remove the materials a selection of strong rod magnets is positioned in a selected geometrical pattern either upstream or downstream the primary solids control equipment. Typical maximum field strength of the surface of Neodymium magnet rods is around 1.2 Tesla. Field experience from offshore operations in the North Sea and land-based drilling operations in the Permian basin have shown how such magnets can be efficient in removal of significant amounts of sub 50 micron magnetic materials that are contaminating drilling fluids. The quantity of the removed magnetic material and the consequent effect of removal of these materials are assessed for impact on cost reductions for downhole equipment wear and improved directional drilling operations. Furthermore, to add to the technology, data from more complicated ditch magnet systems used on offshore rigs are used to help quantify the amounts of detrimental material present in the drilling fluids. In the article, it is presented how modern ditch magnet systems work.
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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".