Innovative Nanoparticle Drilling Fluid and Its Benefits to Horizontal or Extended Reach Drilling
Bibliographic record
Abstract
Abstract Development of new exploration, drilling and completion technologies in the past two decades have led to the opening of new plays in North America targeting unconventional resources. Lots of innovative drilling fluids have been developed and applied in the drilling and completion engineering. The demands encountered drilling highly deviated wells include the effective control of torque and drag. Therefore one of the applications of the innovative drilling fluids is to lower friction coefficient between drillstring and wellbore, which will reduce the drag and make it possible to drill longer horizontal or extended distances during horizontal or extended reach wells. This paper first briefly introduces the problems from the drag when drilling horizontal or extended reach wells, calculation of torque and drag with analytical models and the shortcomings of those models. Then addresses how the lower friction coefficient from the innovative fluid will bring benefit to the horizontal or extended reach drilling, where finite element method (FEM) is introduced to analyze the interaction between the wellbore and drillstring. Finally three case studies are detailed and results are also analyzed. The three examples are calculating the drag and torque along the entire drillstring, comparing the drag and torque with different friction coefficients with constant torque, buoyancy and downhole weight on bit, and obtaining the increment of horizontal section when using the innovative drilling fluid. The tension or compression force distribution along the entire drillstring is clearly shown through graphs or plotting. The results and analysis with FEM show that the low friction coefficient from the innovative drilling fluid has a great benefit in horizontal or extended reach drilling, increasing the current rig and equipment reach limit substantially. The analysis method presented in this paper can provide a solid base for drilling engineers so that they can accurately estimate the extended reach with this new drilling fluid. It will improve the drilling efficiency and reduce the total cost, especially in unconventional well drilling in that less and longer wells can be used to drain the reservoirs.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".