A fixed reduction of all binary interaction coefficients may be a simple practical step to predict phase behavior of complex reservoir fluids. Summary Phase behavior prediction by an equation of state in the near-critical region shows extreme sensitivity to the binary interaction coefficients. For binary mixtures, these coefficients have a systematic decreasing trend as the critical point is approached. Such a trend can be used to calculate the phase behavior of binary mixtures in the critical region. For complex near-critical reservoir fluids, a variable interaction coefficient may not provide satisfactory results. A fixed reduction of the binary interaction coefficients is a simple practical step towards EOS prediction of phase behavior of complex reservoir fluids. Introduction A sizable number of gas condensate and volatile oil reservoirs contain fluids which are near the critical point. Closeness to the critical condition strongly affects the reservoir performance. As an example, GOR production of volatile oil reservoirs increases rapidly below the bubble point pressure. The intensity of the rise in GOR depends on the distance to the critical point. Compositional grading is also pronounced in retrograde condensate and other reservoirs containing near-critical fluids. Equations of state (EOS) are often used to study near-critical reservoir fluids and to carry out compositional simulation. A major deficiency of these equations is their inability to predict reliable vapor liquid equilibrium (VLE) in the critical region. The current industry practice is to adjust several parameters of the EOS to make up for this deficiency. Adjustment of some parameters could be interpreted as changing the critical properties of selected components. The process of tuning is also time consuming and depends on the level of the experience of the user. Tuning when based on VLE data (i.e., bubble point), could be detrimental to volumetric productions. Therefore, both the phase and volumetric data are regressed (adjustment if limited to the interaction coefficients alone, may not require inclusion of volumetric data). Various efforts have been made to improve prediction capability of the popular EOS. These include the move from two-constant to three-constant equations to achieve volumetric calculation accuracy enhancement. Phase behavior of reservoir fluids in the near-critical region is mainly related to VLE prediction capability and, therefore, improvement in volumetric prediction often does not have a significant effect. Besides the line of attack by Coats and Smart who tuned " ", and " " parameters of selected components, as well as selected binary interaction coefficients. Slot-Peterson proposed a different approach. In a study to compute the phase behavior of near-critical reservoir fluids, he concluded that adjustment of various binary interaction coefficients were crucial. Slot-Peterson outlined a systematic approach to obtain various binary-interaction coefficients. In his approach, the binary interaction coefficients of the same pair varied from one reservoir fluid to another. They were also different from the values obtained from the binary VLE data. Slot-Peterson concluded that a large amount of laboratory data (including constant composition expansion, differential liberation, or constant-volume depletion) are crucial to determine the proper binary interaction coefficients for near-critical reservoir fluids. The above short review of the literature shows the desirability of a more predictive capability for the equations of state. In a recent paper, we have demonstrated that near-critical phase behavior prediction of binary and ternary hydrocarbon mixtures follows a systematic decreasing trend as the critical point is approached (at constant temperature). Accounting of the variation of the interaction coefficient is apparently key to near-critical phase behavior prediction. In Ref. 5, we proposed to relate the variation of the interaction coefficient to a distance parameter to the critical point. The dimensionless bulk modulus (related to the inverse of the isothermal compressibility) defined by (Equation 1) was used as the distance parameter. It was shown that " " has the desirable characteristics for linkage to the interaction coefficient. We, then, proposed a functional form for the variation of the interaction coefficient in the form of (Equation 2) where and are constants. Fugacity relations for the Peng Robinson EOS (PR-EOS) based on Eq. 2 were derived. The model (with = 15) was applied to a number of binary and ternary mixtures. The results showed significant improvement in the critical region over the conventional PR-EOS. In this paper, further testing for three binary hydrocarbon systems of C1/n C9, C1/n C20, and C1/n C24, and selected CO2/n-alkanes and N2/n-alkanes systems will be made. The major goal of the paper is, however, to examine the use of the variable interaction coefficient approach (given by Eq. 2) for near-critical reservoir fluids phase behavior prediction by an EOS. Use of an EOS for reservoir fluids and even, simple mixtures requires critical property data. These data are subject to uncertainty. In the following, the reliability of critical data are discussed first. Critical Properties Measured Critical properties of the normal alkanes and isomeric alkanols are available only to normal octadecane (n C18) and (C12), respectively. Estimation of the critical properties for heavier hydrocarbons are from correlations. Four such correlations which are commonly used will be examined. The input to all of these correlations are boiling point temperature (Tb) and specific gravity (Sg). The empirical relations of Lee and Kesler are based on the 3-parameter corresponding states principle. The correlations of Riazi and Daubert for estimation of a critical property are in the simple form of in which the primed parameters are the correlation constants. The empirical relations of Twu...

Percolation and fracture propagation in disordered solids represent two important problems in science and engineering that are characterized by phase transitions: loss of macroscopic connectivity at the percolation threshold $p_c$ and formation of a macroscopic fracture network at the incipient fracture point (IFP). Percolation also represents the fracture problem in the limit of very strong disorder. An important unsolved problem is accurate prediction of physical properties of systems undergoing such transitions, given limited data far from the transition point. There is currently no theoretical method that can use limited data for a region far from a transition point $p_c$ or the IFP and predict the physical properties all the way to that point, including their location. We present a deep neural network (DNN) for predicting such properties of two- and three-dimensional systems and in particular their percolation probability, the threshold $p_c$, the elastic moduli, and the universal Poisson ratio at $p_c$. All the predictions are in excellent agreement with the data. In particular, the DNN predicts correctly $p_c$, even though the training data were for the state of the systems far from $p_c$. This opens up the possibility of using the DNN for predicting physical properties of many types of disordered materials that undergo phase transformation, for which limited data are available for only far from the transition point.

Wearable alcohol monitoring devices demand noninvasive, real-time measurement of blood alcohol content (BAC) reliably and continuously. A few commercial devices are available to determine BAC noninvasively by detecting transcutaneous diffused alcohol. However, they suffer from a lack of accuracy and reliability in the determination of BAC in real time due to the complex scenario of the human skin for transcutaneous alcohol diffusion and numerous factors (e.g., skin thickness, kinetics of alcohol, body weight, age, sex, metabolism rate, etc.). In this work, a transcutaneous alcohol diffusion model has been developed from real-time captured data from human wrists to better understand the kinetics of diffused alcohol from blood to different skin epidermis layers. Such a model will be a footprint to determine a base computational model in larger studies. Eight anonymous volunteers participated in this pilot study. A laboratory-built wearable blood alcohol content (BAC) monitoring device collected all the data to develop this diffusion model. The proton exchange membrane fuel cell (PEMFC) sensor was fabricated and integrated with an nRF51822 microcontroller, LMP91000 miniaturized potentiostat, 2.4 GHz transceiver supporting Bluetooth low energy (BLE), and all the necessary electronic components to build this wearable BAC monitoring device. The %BAC data in real time were collected using this device from these volunteers’ wrists and stored in the end device (e.g., smartphone). From the captured data, we demonstrate how the volatile alcohol concentration on the skin varies over time by comparing the alcohol concentration in the initial stage (= 10 min) and later time (= 100 min). We also compare the experimental results with the outputs of three different input profiles: piecewise linear, exponential linear, and Hoerl, to optimize the developed diffusion model. Our results demonstrate that the exponential linear function best fits the experimental data compared to the piecewise linear and Hoerl functions. Moreover, we have studied the impact of skin epidermis thickness within ±20% and demonstrate that a 20% decrease in this thickness results in faster dynamics compared to thicker skin. The model clearly shows how the diffusion front changes within a skin epidermis layer with time. We further verified that 60 min was roughly the time to reach the maximum concentration, C_max, in the stratum corneum from the transient analysis. Lastly, we found that a more significant time difference between BAC_max and C_max was due to greater alcohol consumption for a fixed absorption time.

Extensive numerical simulations of rigidity percolation with only central forces in large three-dimensional lattices have indicated that many of their topological properties undergo a first-order phase transition at the rigidity percolation threshold $p_{ce}$. In contrast with such properties, past numerical calculations of the elastic moduli of the same lattices had provided evidence for a second-order phase transition. In this paper we present the results of extensive simulation of rigidity percolation in large body-centered cubic (bcc) lattices, and show that as the linear size $L$ of the lattice increases, the elastic moduli close to $p_{ce}$ decrease in a stepwise, discontinuous manner, a feature that is absent in lattices with $L<30$. The number and size of such steps increase with $L$. As $p_{ce}$ is approached, long-range, nondecaying orientational correlations are built up, giving rise to compact, nonfractal clusters. As a result, we find that the backbone of the lattice at $p_{ce}$ is compact with a fractal dimension $D_{bb}\approx 3$. The absence of fractal, scale-invariant clusters, the hallmark of second-order phase transitions, together with the stairwise behavior of the elastic moduli, provide strong evidence that, at least in bcc lattices, many of the topological properties of rigidity percolation as well as its elastic moduli may undergo a first-order phase transition at $p_{ce}$. In relatively small lattices, however, the boundary effects interfere with the nonlocal nature of the rigidity percolation. As a result, only when such effects diminish in large lattices does the true nature of the phase transition emerge.