Comprehensive Experimental Study on the Gas Breakthrough Pressure and Its Implication for the Reservoir Performance

AbstractIt is challenging to interpret the gas breakthrough mechanisms, controlling factors, and its relationships with the reservoir parameters for unconventional reservoirs such as the gas shale, due to the accumulation characteristics of source-reservoir integration. Take the typical marine shale gas of the B field for example, we use the step-by-step (SBS) test to measure the gas breakthrough pressure of the water saturated shales, and investigate the influential factors such as the pore size distribution, mineral composition, and organic geochemical properties. Moreover, the implication of the gas breakthrough capability for the reservoir quality such as the porosity, permeability, the gas content, and the gas occurrence state are addressed. Based on our work, it is observed that the gas breakthrough capability in shale is influenced by many factors. Generally, the gas breakthrough pressure is positively with the amount of ductile minerals such as the clay and the plagioclase, but negatively with the amount of brittle minerals such as the quartz. In addition, the gas breakthrough pressure is decreased with the increase of the pore radius and the specific surface areas. What is more, the influences of geochemical properties on the gas breakthrough capability should not be neglected. Due to the development of organic pores in the kerogen, the gas breakthrough pressure is found to decrease with the increase of the total organic carbon content (TOC) and the residual carbon content (RC). The breakthrough pressure can be used as the significant parameter to indicate the reservoir quality of the shale gas. It is shown that the breakthrough pressure is inversely with the porosity, permeability, the total gas content, and the adsorbed gas content. It is practical and meaningful to measure and estimate the breakthrough pressure for the formation evaluation in shale gas reservoirs

Operation optimization considering multiple uncertainties for the multi-energy system of data center parks based on information gap decision theory

With the rapid growth of the digital economy, data centers have emerged as significant consumers of electricity. This presents challenges due to their high energy demand but also brings opportunities for utilizing waste heat. This paper introduces an operation optimization method for multi-energy systems with data centers, leveraging the information gap decision theory (IGDT) to consider various uncertainties from data requests and the environment. First, a model is established for the operation of a multi-energy system within data centers, considering the integration of server waste heat recovery technology. Second, IGDT is employed to address uncertainties of photovoltaic output and data load requests, thereby formulating an optimal energy management strategy for the data center park. Case studies demonstrate that the electricity purchase cost increased by 5.3%, but the total cost decreased by 30.4%, amounting to 5.17 thousand USD after optimization. It indicates that the operational strategy effectively ensures both efficient and cost-effective power supply for the data center and the park. Moreover, it successfully mitigates the risks associated with fluctuations in data load, thus minimizing the possibility of data load abandonment during uncertain periods

Electric Vehicle Infrastructure Planning and Charging Scheduling Based on Coupled Transportation and Distribution Networks Considering Environmental Impacts

During the past few decades, environmental protection and climate change have become a growing concern for human society. Numerous studies cite the transportation sector and the power sector as the two largest sources of pollution and carbon emissions. To address environmental issues and achieve sustainable development for human society, electric vehicles (EVs) have emerged as an ideal mode of transportation and an alternative to conventional vehicles. To promote the further development and popularisation of EVs in higher efficiency, higher positive environmental effects, and higher reliability, much of the foreseen attention will focus on charging infrastructure planning and charging scheduling based on coupled transportation and power networks. The focus of the thesis is on charging infrastructure planning and charging scheduling under coupled transportation and power distribution networks, which consider environmental impacts and thereby promote the diffusion of EVs economically and environmentally. The first part of the thesis presents the changes in emissions from the adoption and charging of EVs as well as the classification of emissions from a traffic-power system perspective. The second part aims to study the planning of charging infrastructure for the different development stages of transportation electrification and grid decarbonisation. The last part investigates charging scheduling based on EV path selection in the real-time traffic system and load management in the power system. The strategies, methods, and mathematical models proposed in this thesis are demonstrated through simulation cases, providing effective solutions to the challenges raised by the popularisation of EVs. These solutions can facilitate the electrification of transportation and the decarbonisation of the grid

A novel navigation and charging strategy for electric vehicles based on customer classification in power-traffic network

With electric vehicles getting increasingly popular, there has been a lot of interest in encouraging future electric vehicle development in terms of greater charging efficiency, greater benefits to the environment, and greater reliability. One of the most prominent research is on the scheduling of electric vehicle charging and navigation. High-quality navigation and charging scheduling strategy must depend on the state of the coupled network between the transportation and power networks, while the outcomes of scheduling can also have a significant impact on these networks. In this paper, we propose a novel dynamic navigation and charging strategy that fully considers the coupled traffic and power distribution network. Firstly, electric vehicles with similar travel and charging requirements are classified. Secondly, an elastic scheme including fixed route navigation and flexible charging scheduling at charging stations is provided. We also propose an information integration framework for the implementation of electric vehicle routing and charging scheduling, information interaction between sectors including the status of heterogeneous electric vehicles, day-ahead power scheduling, and base load demands. The novel strategy aims at optimal electric vehicle navigation and charging at the system level achieving a lower overall travel cost, charging cost, carbon emission, and load variance of the power distribution network. Classification algorithms, stochastic dynamic programming, and queuing theory are used for mathematical modeling. Numerical results demonstrate the effectiveness of the proposed novel strategy for the navigation and charging scheduling of electric vehicles

A joint planning strategy for EV charging system towards net-zero transportation electrification

Electric vehicles (EVs) are pivotal in the progression toward sustainable transportation. However, a growing body of recent studies has challenged the often-assumed notion that electric vehicles are often considered to be net-zero emissions. These studies suggest that electric vehicles could potentially generate substantial emissions due to their reliance on fossil fuels to generate electricity, especially when receiving fast charging services. This phenomenon has been mitigated with the use of renewable energy sources in the power system. However, due to the intermittent nature of renewable energy sources, additional requirements have arisen. To address this situation, this paper presents an interdisciplinary study of collaborative fast-charging stations (FCS) and distributed renewable energy planning based on the interactions between traffic and power networks in order to increase the adoption rate of electric vehicles and reduce emissions caused by traffic and power systems. The locations and sizing of fast-charging stations, distributed photovoltaic generation, and renewable energy power supply for electric vehicles are determined in the proposed strategy through multi-objective integer programming. The proposed planning strategy considers heterogeneous electric vehicle driving range constraints, the operation security of the power distribution system, the reliability and expansion of the current power distribution, as well as different types of air pollutant classifications resulting from the charging behaviors. To guarantee the feasibility and accuracy of the planning results, an interactive algorithm is applied to solve the multi-objective integer model. Several cases are conducted to validate the proposed approach. The results demonstrate that the proposed planning strategy has good performance in the planning of fast-charging stations and distributed renewable energy integration.</p

Improvement of GNSS Carrier Phase Accuracy Using MEMS Accelerometer-Aided Phase-Locked Loops for Earthquake Monitoring

When strong earthquake occurs, global navigation satellite systems (GNSS) measurement errors increase significantly. Combined strategies of GNSS/accelerometer data can estimate better precision in displacement, but are of no help to carrier phase measurement. In this paper, strong-motion accelerometer-aided phase-locked loops (PLLs) are proposed to improve carrier phase accuracy during strong earthquakes. To design PLLs for earthquake monitoring, the amplitude-frequency characteristics of the strong earthquake signals are studied. Then, the measurement errors of PLLs before and after micro electro mechanical systems (MEMS) accelerometer aiding are analyzed based on error models. Furthermore, tests based on a hardware simulator and a shake table are carried out. Results show that, with MEMS accelerometer aiding, the carrier phase accuracy of the PLL decreases little under strong earthquakes, which is consistent with the models analysis