Seismic history matching using proxy models

Generally, reservoir simulation is used to predict field performance and analyse uncertainties for assistance in decision making, while history matching is a key step in reservoir simulation, which is a process of model adjustment and simulation runs with different reservoir parameter settings until the differences between simulated data and historical data reach minima. An efficient reservoir simulation model must be the one that can predict reservoir performance and update history matching results continuously by modifying the reservoir model as long as new data become available. However, reservoir simulation can be very time consuming, depending on the complexity of the reservoir model, and history matching is even more computationally expensive, since it requires many simulation runs. Recently, intelligent technology advances in the oil and gas industry, have initiated a new era of big data. As different varieties of data have been integrated to make better decisions, together with the generation of high frequency data streams, a major concern for petroleum engineers is how reservoir simulation should be calibrated in line with the real time data without compromising the simulation time. In the seismic history matching (SHM) workflow this may be a more obvious issue than in the conventional well production history matching. In order to address this problem, many studies have been undertaken. Besides increasing computational power, various types of research have focused on speeding up the reservoir simulation process, especially history matching, by either implementing optimisation algorithms or generating efficient proxy models. Nevertheless, there has not yet been a standard method recognized in reservoir simulation. In this study, a novel method has been proposed as an attempt to investigate the possibility of achieving efficient seismic history matching by data-driven proxy models. This thesis essentially involves detailing background motivations, proxy model building, followed by its testing and application in SHM. Comparisons of proxy models with conventional simulators have been made from different aspects. The objective is mainly focused on examining the capability of the proxy models as a simplification of the conventional physics-based simulators in SHM. According to the simulation results, the feasibility of the combination of proxy models has been proven to be successful and efficient. Importantly, huge amounts of time and effort have been saved in the reservoir simulation process. In addition, it is suggested that other challenges of SHM, such as multi-domain comparison and multi-field communication, could be tackled by using the proxy method

Computational methods in their application to optical materials

Fast development of computer facilities and quantum chemical calculations made computational materials science be a very important tool in modern research aimed at design, development and understanding of novel functional materials with enhanced performance. This special issue is focused on applications of various computational methods to the description of physical properties of optical materials. Density Functional Theory (DFT)-based computational techniques, semiempirical crystal field models, machine learning and other tools used for explanation of experimental results, deeper understanding of optical materials properties and smart search for new materials with advanced characteristics are discussed in the special issue papers. All authors of these selected papers are well-known scientists who made solid contributions to their respective fields of research. Wide range of presented methods and approaches, together with broad scope of the considered materials and phenomena (phosphors, optical thermometers, radiative and non-radiative transitions) will make this collection of papers an interesting and valuable source of scientific information for an audience ranging from postgraduate students to experienced researchersCorrected Proo

Study of pesudoscalar transition form factors within light front quark model

We study the transition form factors of the pesudoscalar mesons ($\pi,\eta$ and $\eta^{\prime}$) as functions of the momentum transfer $Q^2$ within the light-front quark model. We compare our results with the recent experimental data by CELLO, CLEO, BaBar and Belle. By considering the possible uncertainties from the quark masses, we illustrate that our predicted form factors can fit with all the data, including those at the large $Q^2$ regions.Comment: 10 pages, 4 figures, accepted for publication in Phys. Rev.

Experimental Study of the Jet Engine Exhaust Flow Field of Aircraft and Blast Fences

A combined blast fence is introduced in this paper to improve the solid blast fences and louvered ones. Experiments of the jet engine exhaust flow (hereinafter jet flow for short) field and tests of three kinds of blast fences in two positions were carried out. The results show that the pressure and temperature at the centre of the jet flow decrease gradually as the flow moves farther away from the nozzle. The pressure falls fast with the maximum rate of 41.7%. The dynamic pressure 150 m away from the nozzle could reach 58.8 Pa, with a corresponding wind velocity of 10 m/s. The temperature affected range of 40°C is 113.5×20 m. The combined blast fence not only reduces the pressure of the flow in front of it but also solves the problems that the turbulence is too strong behind the solid blast fences and the pressure is too high behind the louvered blast fences. And the pressure behind combined blast fence is less than 10 Pa. The height of the fence is related to the distance from the jet nozzle. The nearer the fence is to the nozzle, the higher it is. When it is farther from the nozzle, its height can be lowered

π0→γ∗γ\pi^0\to\gamma^*\gamma transition form factor within Light Front Quark Model

We study the transition form factor of $\pi^0\to\gamma^* \gamma$ as a function of the momentum transfer $Q^2$ within the light-front quark model (LFQM). We compare our result with the experimental data by BaBar as well as other calculations based on the LFQM in the literature. We show that our predicted form factor fits well with the experimental data, particularly those at the large $Q^2$ region.Comment: 11 pages, 4 figures, accepted for publication in PR