澳门银河赌场注册送38-澳门银河赌场招人好招吗_百家乐德州_全讯网下载 (中国)·官方网站

In deepwater drilling, the properties of water-based drilling fluids change remarkably due to low temperature and high pressure, which have a significant effect on lost circulation, wellbore instability and the window between pore pressure and fracturing pressure. The present work investigates the influence of low temperature and high pressure on polymer and nanoparticle (boron nitride (BN)) based drilling fluids with an aim to improve their rheological properties and fluid loss control. The amplitude and frequency sweep tests were conducted to understand the viscoelastic nature of the samples. The amplitude sweep tests confirmed the structural stability of the designed fluid within the studied sweep frequency. The study reveals that storage modulus (G?) and loss modulus (G?) of the samples are enhanced with increasing concentration of BN nanoparticles. Their viscoelastic range also increases due to the intermolecular interaction within the structure of the fluid in the presence of the nanoparticles. Within the linear viscoelastic range (LVER), all the samples show the dominance of elastic modulus than viscous modulus which delineates the solid-like behaviour. The results of rheological tests of drilling fluid containing BN nanoparticles indicate a significant reduction in plastic viscosity (PV), yield point (YP) and apparent viscosity (AV). The rheological studies conducted at different temperatures (from 10 °C to ?5 °C) and pressures (from 7.8 MPa to11 MPa) reveal the minimum effect of pressure and temperature on the rheology of samples, which are desirable for their applications in hydrate and deepwater drilling. The filtration loss experiments conducted at 30 °C and 0.69 MPa show a large reduction in fluid loss volume (60.6%) and filter cake thickness (90%) for the sample with 0.4 wt% BN nanoparticles compared to that of the sample without nanoparticles. The filter cake permeability is also in the favourable range with 0.008 mD which shows a 94% reduction compared to the sample without nanoparticles. A regression model was developed to mathematically describe the experimental results, which demonstrates a good fitting with the statistical data of fluid loss volume, thickness and permeability of the filter cake.