Molecular dynamics simulation of surfactant reducing MMP between CH4 and n-decane

Reinjecting produced methane offers cost-efficiency and environmental benefits for enhances oil recovery. High minimum miscibility pressure (MMP) in methane-oil systems poses a challenge. To overcome this, researchers are increasingly focusing on using surfactants to reduce MMP, thus enhancing the effectiveness of methane injections for oil recovery. This study investigated the impact of pressure and temperature on the equilibrium interfacial tension of the CH4+n-decane system using molecular dynamics simulations and the vanishing interfacial tension technique. The primary goal was to assess the potential of surfactants in lowering MMP. Among four tested surfactants, ME-6 exhibited the most promise by reducing MMP by 14.10% at 373 K. Key findings include that the addition of ME-6 enriching CH4 at the interface, enhancing its solubility in n-decane, improving n-decane diffusion capacity, CH4 weakens n-decane interactions and strengthens its own interaction with n-decane. As the difference in interactions of n-decane with ME-6's ends decreases, the system trends towards a mixed phase. This research sets the stage for broader applications of mixed-phase methane injection in reservoirs, with the potential for reduced gas flaring and environmental benefits.

FMEM: functional mixed effects modeling for the analysis of longitudinal white matter Tract data

Many longitudinal imaging studies have collected repeated diffusion tensor magnetic resonance imaging data to understand white matter maturation and structural connectivity pattern in normal controls and diseased subjects. There is an urgent demand for the development of statistical methods for the analysis of diffusion properties along fiber tracts and clinical data obtained from longitudinal studies. Jointly analyzing repeated fiber-tract diffusion properties and covariates (e.g., age or gender) raises several major challenges including (i) infinite-dimensional functional response data, (ii) complex spatial-temporal correlation structure, and (iii) complex spatial smoothness. To address these challenges, this article is to develop a functional mixed effects modeling (FMEM) framework to delineate the dynamic changes of diffusion properties along major fiber tracts and their association with a set of covariates of interest and the structure of the variability of these white matter tract properties in various longitudinal studies. Our FMEM consists of a functional mixed effects model for addressing all three challenges, an efficient method for spatially smoothing varying coefficient functions, an estimation method for estimating the spatial-temporal correlation structure, a test procedure with local and global test statistics for testing hypotheses of interest associated with functional response, and a simultaneous confidence band for quantifying the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of FMEM and to demonstrate that FMEM significantly outperforms the standard pointwise mixed effects modeling approach. We apply FMEM to study the spatial-temporal dynamics of white-matter fiber tracts in a clinical study of neurodevelopment.