The application of functionalized/unfunctionalized (multi-walled) carbon nanotubes (CNT) was investigated in the context of formulating nano-based drilling fluids from water/oil-based fluid templates. CNT functionalization was attempted by applying hydrophilic functional groups onto the surface of the nanotubes via acid treatment. Experimental data were collected for thermal conductivity, viscosity/yield point, and filtrate amount in all samples. The time evolution of thermal conductivity was studied, as well as the effects of temperature and CNTs volume fraction on the parameter. Scanning electron microscopy (SEM) was used to monitor CNTs dispersion quality. The thermal conductivity results unveil considerable enhancements, by as much as 23.2 % (1 % vol. functionalized CNT) in CNT-water-based case at ambient temperature, with extended improvement of 31.8 % at an elevated temperature of 50°C. Corresponding results for the CNT-oil-based case exhibit an improvement in thermal conductivity by 40.3 % (unfunctionalized) and 43.1 % (functionalized) and 1 % volume fraction of CNT. The rheological results follow an analogous improvement trend. For the CNT-oil-based case, the filtration tests conducted at 138°C and 500 (psi) show a 16.67 % reduction in filtrate amount (1 % vol. CNT). The time evolution of thermal conductivity was found to nearly equalize (at an amount of 9.7 %) after 100 h of sample preparation in both functionalized and unfunctionalized CNT-oil-based cases.

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This article develops a Bayesian framework to quantify the absolute permeability of water in a porous structure from the geometry and clustering parameters of its underlying pore-throat network. These parameters include the network`s diameter, transivity, degree, centrality, assortativity, edge density, K-core decomposition, Kleinberg’s hub centrality scores, Kleinberg's authority centrality scores, length, and porosity. In addition, the incorporated clustering aspects of the networks have been determined with respect to several clustering criteria – edge betweenness, greedy optimization of modularity, multi-level optimization of modularity, and short random walks. As such, the article takes the first footsteps of creating a Database of Micro Networks for micro-scale porous structures, to be used as main input stream for the proposed Bayesian scheme. Doi: 10.28991/HIJ-2020-01-04-02 Full Text: PDF

Prevention of asphaltene formation in reservoir rocks can result in resolving a severe long-lasting issue in petroleum production. The present research addresses the issue in the context of exploring the potential effect of nickel oxide (NiO) nanoparticles in destabilizing asphaltene deposition in porous media, in the presence of carbon dioxide. To ensure proper distribution within the system and to retain future field-scale applicability, the NiO nanoparticles were exposed to the in situ oil via injection gas stream, in which they had been uniformly dispersed using polydimethylsiloxane (PDMS). The experimental results, established under miscible CO 2 state, indicate a considerable improvement in permeability/porosity reduction of core, as well as less asphaltene accumulation in porous media and increased oil recovery factor after applying NiO nanoparticles.

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A set of molecular dynamics simulations was conducted, as the first comparative study of the adsorption behavior of liquid hydrocarbon/acid gases/water molecules over f10 14g calcite surface and {001} octahedral kaolinite surface in nanoconfined slit. According to atomic z-density profiles, hydrocarbon molecules have higher tendency towards the f10 14g calcite surface than the {001} octahedral kaolinite surface. In addition, water molecules form stronger adsorption layer over calcite surface than kaolinite. In contrast, acid gas molecules have higher tendency towards kaolinite surface than calcite. This behavior was spotted within nanometer-sized slit pores. The results also point to reduction in self-diffusion coefficient of molecules with strong adsorption over mineral surfaces in nano-confined environment.

We employ the grand canonical Monte Carlo simulation technique to investigate the influence of charged nanoparticles (macro-ions) on the force between colloidal objects. Specifically, the structure and osmotic pressure of a system of screened Coulomb (Yukawa) particles confined between charged planar walls are simulated. We observe osmotic pressure to oscillate with wall separation and these oscillations to correspond to changes in the number of nanoparticle layers present in the slit pore. Using the Derjaguin approximation, we estimate the overall force between a colloidal sphere and a flat surface and compare our predictions to recent atomic force microscopy (AFM) results (Tulpar, A.; Van Tassel, P. R.; Walz, J. Y. Langmuir 2006, 22, 2876-2883). In excellent agreement with experiment, we find the wavelength of the force versus distance oscillations to scale as c(nu), with c being the bulk nanoparticle concentration and nu = -0.31 +/- 0.01; that is, slightly lower in magnitude from the expected value -1/3 based on average molecule spacing. By considering an order parameter measuring the extent to which neighboring particles form hexagonal symmetry, we show structural order within confined nanoparticle systems to be significantly enhanced as compared to that of bulk systems, despite being quite insensitive to wall separation. Wavelength scaling and order parameter analysis together suggest the confined macro-ion systems to be somewhat glasslike.

Abstract. The multiphase flow through wellhead restrictions of an offshore oil field in Iran is investigated and two sets of new correlations are presented for high flow rate and water cut conditions. The both correlations are developed by using 748 actual data points, corresponding to critical flow conditions of gas-liquid mixtures through wellhead chokes. The first set of correlations is a modified Gilbert equation and predicts liquid flow rates as a function of flowing wellhead pressure, gas-liquid ratio and surface wellhead choke size. To minimize error in such condition, in the second correlation, free water, sediment and emulsion (BS & W) is also considered as an effective parameter. The predicted oil flow rates by the new sets of correlations are in the excellent agreement with the measured ones. These results are found to be statistically superior to those predicted by other relevant published correlations. The both proposed correlations exhibit more accuracy (only 2.95% and 2.0% average error, respectively) than the existent correlations. These results should encourage the production engineer which works at such condition to utilize the proposed correlations for future practical answers when a lack of available information, time, and calculation capabilities arises.