Hollow-Glass Sphere Application in Drilling Fluids: Case Study
Bibliographic record
Abstract
Abstract In an effort to continuously optimize drilling operations and economics, an operator examined the impact of adding hollow-glass spheres (HGS) directly to the drilling fluid formulation instead of performing underbalanced drilling operations. Both nitrogen and HGS were believed to reduce hydrostatic pressure of the mud column in the hole, resulting in higher drilling rates of penetration (ROP) and fewer mud losses to the wellbore. Invert emulsion fluid was blended with HGS on three drilling rigs using a specialized, environmentally acceptable mixing system to help reduce density. HGS concentration and fluid density were monitored and maintained while drilling over the period of one to three weeks for these three wells. The proper type of centrifuge and its setup is fundamental to maintaining low-gravity solids (LGS) at an acceptable level for proper fluid management while maintaining the desired concentration of HGS. Drilling mud density was reduced, as expected, and mud loss was minimized as a result. Overall costs were reduced because of less invert emulsion fluid loss, a reduction in the amount of lost-circulation material mixed compared to previous wells in the area, and less time spent on the drilling rig attributed to not having to stop to mix lost-circulation material or prepare new invert emulsion fluid volumes to replace losses. A significant ROP increase was not observed during this trial. The addition of HGS is a cost-effective solution to help reduce fluid loss in the Kakwa field of the Western Canadian Sedimentary basin. This trial helped reduce overall rig time, and fluid loss experienced was less than in previous wells in which this technology was not used. This paper provides information on HGS as an economic alternative to nitrogen to help reduce the hydrostatic pressure of invert emulsion drilling fluids using a small-footprint, environmentally acceptable mixing system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".