Bibliographic record
Abstract
This article, written by Editorial Manager Adam Wilson, contains highlights of paper SPE 156992, ’Novel Nanoparticle-Based Drilling Fluid With Improved Characteristics,’ by Mohammad F. Zakaria, Maen Husein, and Geir Hareland, SPE, University of Calgary, prepared for the 2012 SPE International Oilfield Nanotechnology Conference and Exhibition, Noordwijk, The Netherlands, 12-14 June. The paper has not been peer reviewed. A new class of nanoparticle lost-circulation material (LCM) has been developed. Two different approaches of nanoparticle formation and addition to oil-based drilling fluid have been tested. All nanoparticles were prepared in house, either within the oil-based drilling fluid (in situ) or within an aqueous phase (ex situ), with the latter being eventually blended with the drilling fluid. Under a low-pressure/low-temperature (LP/LT) American Petroleum Institute standard test, more than 70% reduction in fluid loss was achieved in the presence of nanoparticles, compared with only 9% reduction in the presence of typical LCMs. Introduction LCMs with diameters in the range of 0.1–100m may play an important role when the cause of fluid loss occurs in 0.1-Μm to 1-mm porous formation. In practice, however, the size of pore openings in shales that may cause fluid loss varies in the range of 10 nm–0.1 m, where nanoparticles as LCM could fulfill the specific requirements by virtue of their size domain, hydrodynamic properties, and interaction potential with the formation. Nanoparticles are defined as particulate dispersions or solid particles with a size in the range of 1–100 nm. These particles are smaller than microparticles, have a high surface/volume ratio, and may provide superior fluid properties at low concentrations of the additives. The main application of nanoparticles would be to control the spurt and fluid loss into the formation and, hence, control formation damage. The presence of nanoparticles can lead to better sealing at an earlier stage of filter-cake formation and, subsequently, a thinner impermeable mudcake. Because of their high surface/volume ratio, the particles in the mudcake matrix can be removed easily by traditional cleaning systems during completion. Thus, the nanoparticles can be used as rheology modifiers, fluid-loss additives, and shale inhibitors at low concentrations without the fear of particles lingering in the drilled well.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".