We have demonstrated a hybrid flow-to-batch process for synthesizing monodisperse poly(styrene-co-acrylamide) nanoparticles via a surfactant-free emulsion radical polymerization. The flow-to-batch synthesized nanoparticles have a smaller average particle size, tighter particle size distribution, higher molecular weight, and lower molecular weight distribution compared to conventionally batch-synthesized nanoparticles. Our results also indicate that the flow-to-batch synthesized nanoparticles have more hydrophilic acrylamide segments on the particle surface than the batch-synthesized nanoparticles. These results demonstrate that a flow synthesis process can improve the quality of nanoparticles due to the efficient mixing and heat transfer in a flow reactor and simplify the scale up of nanoparticle synthesis in a conventional chemistry lab.
Mud logging is frequently performed during oil and gas operations for determining the position of hydrocarbons and accurately assessing the geophysical properties of the formation based on the study of returned drill cuttings. The successful application of an advanced mud logging analysis depends on an accurate determination of the depth where the drill cuttings were generated. We have envisioned an improved accuracy of cuttings’ depth assignment via labeling the drill cuttings with barcoded polymeric nanoparticles (NPs) when they are generated at the drill bit. However, the detection of NPs at sub-ppm levels in very complex and viscous drilling muds is a challenge. This paper reports a unique workflow comprising cleaning, solvent extraction, thermal separation, and pyrolysis-gas chromatography–mass spectrometry (Py-GCMS) detection of polymeric NPs in the drilling mud and on the collected drill cuttings. Applying this workflow to the drill cuttings successfully removes most GCMS matrix interferences generated from the drilling mud. Our method has unambiguously detected and quantified nanograms of NPs on the cuttings recovered from a field test that probed the injection of ppm levels of the NPs into the drilling fluid while drilling a carbonate gas well.
During a drilling operation, rock cuttings are often sampled off a shale shaker for lithology and petrophysical characterization. These analyses play an important role in describing the subsurface; and it is important that the depth origin of the cuttings be accurately determined. Traditionally, mud-loggers determine the depth origin of the sampled cuttings by calculating the lag time required for the cuttings to travel from the bit to the surface. These calculations, however, can contain inaccuracies in the depth correlation due to the shuffling and settling of cuttings as they travel with drilling fluid to the surface, due to unplanned conditions like drilling an overgauge hole, and due to other unforeseen drilling events, especially critical in horizontal sections. We therefore aimed to remedy these inaccuracies by developing a series of styrene-based nanoparticles that tagged the cuttings as they were generated at the drillbit. These “NanoTags” were tested while drilling in Q4, 2019; and the results indicated that the NanoTags did in fact have the potential to identify some systematic errors compared with traditional mud logging calculations.
Manual sampling rock cuttings off the shale shaker for lithology and petrophysical characterization is frequently performed during mud logging. Knowing the depth origin where the cuttings were generated is very important for correlating the cuttings to the petrophysical characterization of the formation. It is a challenge to accurately determine the depth origin of the cuttings, especially in horizontal sections and in coiled tubing drilling, where conventional logging while drilling is not accessible. Additionally, even in less challenging drilling conditions, many factors can contribute to an inaccurate assessment of the depth origin of the cuttings. Inaccuracies can be caused by variation of the annulus dimension used to determine the lag time (and thus the depth of the cuttings), by the shifting or scrambling of cuttings during their return trip back to the surface, and by the mislabelling of the cuttings during sampling. In this work, we report the synthesis and application of polystyrenic nanoparticles (NanoTags) in labeling cuttings for depth origin assessment. We have successfully tagged cuttings using two NanoTags during a drilling field test in a carbonate gas well and demonstrated nanogram detection capability of the tags via pyrolysis-GCMS using an internally developed workflow. The cuttings depth determined using our tags correlates well with the depth calculated by conventional mud logging techniques.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.