Multi-scale simulation of multiphase multi-component flow in porous media using the Lattice Boltzmann Method

This thesis consists of work mainly performed within the Qatar Carbonates and Carbon Storage Research Centre (QCCSRC) project, focusing on the prediction of flow and transport properties in porous media. The direct pore scale simulation of complex fluid flow on reservoir rocks is the main topic of this work. A simulation package based on the lattice Boltzmann method has been developed to study single and multiphase flow as well as thermal and solute dispersion in porous media. The simulator has been extensively validated by comparing simulation results to reference solutions. Various numerical experiments have been performed to study the single/multiphase/solute dispersion flow in reservoir rocks. The simulator successfully predicts various transport properties including single phase and relative permeability, capillary pressure, initial-residual saturation, residual cluster size distribution and dispersion coefficient. The prediction has been compared to available experimental data and was generally found to be in good agreement. The simulator is also ready for exploring the two-phase dynamic problem with coupled and nonlinear physical processes including the effect of wettability, surface tension and hysteresis. To improve the efficiency of the lattice Boltzmann simulations, an optimised collision model and corresponding parallel operation are proposed and implemented. A sparse storage scheme which significantly reduces the memory requirement has been designed and implemented for complex porous media. Due to the application of these optimisation schemes, it is possible to perform simulations on large scale samples (Size >1024x512x512). The optimised code shows very good and promising performance, and nearly ideal scalability was achieved.Open Acces

The Influence of Hybrid Aggregates on Different Types of Concrete

This chapter presents different experimental results regarding the influences of normal-weight sand and lightweight sand (shale pottery (SP)) on different types of concrete. Because of the porosity of lightweight aggregates (LWAs), which can absorb and release water in concrete, the effect of concrete curing is better, and thus the properties of concrete are improved. On the other hand, because the lightweight coarse aggregate (LWCA) rises easily in all-lightweight concrete (ALWC) during pumping and vibration and the cost of ALWC is also higher, a method of replacing part of the lightweight aggregates in ALWC with normal-weight aggregates is used. These new types of concretes include sand lightweight concrete (SLWC), gravel lightweight concrete (GLWC), hybrid aggregate lightweight concrete (HALWC), and so on. This chapter mainly discusses the properties of lightweight aggregate concrete (LWAC), lightweight sand foamed concrete, lightweight sand mortar, and reinforced LWAC. The chapter also includes LWAC of high temperature, low temperature, durability, and uni- and multiaxial mechanical properties according to the results of our research group over recent decades. All of the experimental results show that the properties can meet Chinese National Code requirements

An Automatic In Situ Contact Angle Determination Based on Level Set Method

Open Access via the Jisc Wiley agreement Acknowledgments. We would like to thank Total for financial support and permission to publish this work and Mohamed Regaieg from Total to provide us with the mixed case sandstone image data. We would like to thank Richard Rivenq and Ritesh Kumar for their fruitful discussions during the course of this research, and their comments on manuscript. We also thank Kamaljit Singh and Martin J. Blunt for sharing their experimental dataset through the Digital Rock Portal (the data could be accessed through https://www.digitalrocksportal.org/projects/125).Peer reviewedPublisher PD

Option Pricing Model Based on Newton-Raphson Iteration and RBF Neural Network Using Implied Volatility

As option is a kind of significant financial derivatives, option pricing will affect both the risk and profit of the investment. This paper proposed an option pricing model based on RBF neural network combined with the Newton-Raphson iteration method which is used to obtain the implied volatility.First, considering implied volatility includes investors’ expectation about the changes of future price options. Newton-Raphson iteration method is used to obtain the implied volatility by rolling estimation which is also added into the RBF neural network model. Then, RBF neural network is trained based on Black-Scholes model. Self-organizing learning and the least square method are used to optimize the parameters of RBF neural network.At last, empirical study and analysis with 10 50ETF stock options chosen from Shanghai Stock Exchange market have been performed, the result shows that the accuracy of the proposed model is better than the traditional BP neural network and B-S model and the effect of option pricing using by implied volatility is also better than others

Pore-scale modelling of elastic properties in hydrate-bearing sediments using 4-D synchrotron radiation imaging

Acknowledgments The financial support for this study was from PetroChina. We also thank S. K. Sahoo for sharing the high resolution 4D synchrotron radiation imaging data of hydrate formation.Peer reviewedPublisher PD