Insights from molecular dynamics on CO2 diffusion coefficient in saline water over a wide range of temperatures, pressures, and salinity: CO2 geological storage implications

Sequence-to-Sequence Change-Point Detection in Single-Particle Trajectories via Recurrent Neural Network for Measuring Self-Diffusion

A recurrent neural network is developed for segmenting between anomalous and normal diffusion in single-particle trajectories. Accurate segmentation infers a distinct change point that is used to approximate an Einstein linear regime in the mean-squared displacement curve via the transition density function, a unique physical descriptor for short-lived and delayed transiency. Through several artificial and simulated scenarios, we demonstrate the compelling accuracy of our model for dissecting linear and nonlinear behaviour. The inherent practicality of our model lies in its ability to substantiate the self-diffusion coefficient through offline trajectory segmentation, which is opposed to the common ‘best-guess’ linear fitting standard. Additionally, we show that the transition density function has fundamental implications and correspondence to underlying mechanisms that influence transition. In particular, we show that the known proportionality between salt concentration and diffusion of water also influences delayed anomalous behaviour.

Evolving Machine Learning Methods for Density Estimation of Liquid Alkali Metals over the Wide Ranges

Alkali metals are widely used as industrial materials in products such as electrochemical cells because of their properties that make them suited to high temperatures. In this study, three computational approaches including gene expression programming (GEP), least squares support vector machine (LSSVM), and adaptive neuro fuzzy inference system (ANFIS) have been suggested to estimate density of different liquid alkali metals in extensive ranges of pressure and temperature. An experimental databank involving 595 experimental alkali metals’ densities has been gathered to prepare and test the models. The mathematical and visual comparisons of these models’ outputs and real density values are used to assess capacities of GEP, LSSVM, and ANFIS models in prediction of alkali metals’ density. The determined R-squared values for GEP, LSSVM, and ANFIS are 0.9999, 1, and 1, respectively. The MSE values are estimated to be 0.9184, 0.815, and 0.154 for GEP, ANFIS, and LSSVM, respectively. According to these results, these models can be suggested as simple and accurate ways for determining alkali metals’ properties. Results showed that LSSVM has the best performance in comparison with GEP and ANFIS. Moreover, the parametric analysis of input parameters is carried out to show the impact of them on alkali metals’ density. According to this analysis, the amount of lithium can be the most effective parameter on the mixture density.

A systematic and critical review of application of molecular dynamics simulation in low salinity water injection

Low Salinity Water Injection (LSWI) has been a well-researched EOR method, with several experimental and theoretical scientific papers reported in the literature over the past few decades. Despite this, there is still an ongoing debate on dominant mechanisms behind this complex EOR process, and some issues remain elusive. Part of the complexity arises from the scale of investigation, which spans from sub-pore scale (atomic and electronic scale) to pore scale, core scale, and reservoir scale. Molecular Dynamics (MD) simulation has been used as a research tool in the past decade to investigate the nano-scale interactions among reservoir rock (e.g., calcite, silica), crude oil, and brine systems in presence of some impurities (e.g., clay minerals) and additives (e.g., nanoparticles). In this paper, fundamental concepts of MD simulation and common analyses driven by MD are briefly reviewed. Then, an overview of molecular models of the most common minerals encountered in petroleum reservoirs: quartz, calcite, and clay, with their most common types of potential function, is provided. Next, a critical review and in depth analysis of application of MD simulations in LSWI process in both sandstone and carbonate reservoirs in terms of sub-pore scale mechanisms, namely electrical double layer (EDL) expansion, multi-ion exchange (MIE), and cation hydration, is presented to scrutinize role of salinity, ionic composition, and rock surface chemistry from an atomic level. Some inconsistencies observed in the literature are also highlighted and the reasons behind them are explained. Finally, a future research guide is provided after critically discussing the challenges and potential of the MD in LSWI to shed more light on governing mechanisms behind LSWI by enhancing the reliability of MD outcomes in future researches. Such insights can be used for design of new MD researches with complementary experimental studies at core scale to capture the main mechanisms behind LSWI.