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The ultimate goal of oilfield development is to maximize the investment benefits. The reservoir performance prediction is directly related to oilfield investment and management. The traditional strategy based on numerical simulation has been widely used with the disadvantages of long run time and much information needed. It is necessary to form a fast and convenient method for the oil production prediction, especially for layered reservoir. A new method is proposed to predict the development indexes of multi-layer reservoirs based on the injection-production data. The new method maintains the objectivity of the data and demonstrates the superiority of the intelligent algorithm. The layered reservoir is regarded as a series of single layer reservoirs on the vertical direction. Considering the starting pressure gradient of non-Newtonian fluid flow and the variation of water content in the oil production index, the injection-production response model for single-layer reservoirs is established. Based on that, a composite model for the multi-layer reservoir is established. For model solution, particle swarm optimization is applied for optimization of the new model. A heterogeneous multi-layer model was established for validation of the new method. The results obtained from the new proposed model are in consistent with the numerical simulation results. It saves a lot of computing time with the incorporation of the artificial intelligence methods. It showed that this technique is valid and effective to predict oil performance in layered reservoir. These examples showed that the application of big data and artificial intelligence method is of great significance, which not only shortens the working time, but also obtains relatively higher accuracy. Based on the objective data of the oil field and the artificial intelligence algorithm, the prediction of oil field development data can be realized. This technique has been used in nearly 100 wells of Bohai oilfields. The results showed in this paper reveals that it is possible to estimate the production performance of the water flooding reservoirs.

Part 4: Threats; International audience; Attack trees are used in cybersecurity analysis to give an analyst a view of all the ways in which an attack can be carried out. Attack trees can become large, and developing them by hand can be tedious and error-prone. In this paper the automated generation of attack trees is considered. The method proposed is based on a library of attack templates – parameterisable patterns of attacks such as denial of service or eavesdropping – and that also uses an abstract model of the network architecture under attack. A pseudocode implementation of the method is also presented. The example application given is from the automotive domain and using an architecture consisting of linked CAN networks – a network configuration found in virtually every current vehicle.

Automated Vehicles (AVs) are expected to help in significantly reducing traffic injuries and fatalities in the near future. However, to achieve this goal, they must be safe and secure. The recent news of the first fatal crash of AV including pedestrian confirm the urgent need of addressing AV safety and security issues to prevent such accidents from happening in the future. In order to outperform human drivers, AVs need to communicate with the other traffic participants, which makes them more vulnerable to cyberattacks. Cooperative Intelligent Transport Systems (C-ITS), which include vehicle-to-vehicle and vehicle-to-infrastructure communications, are expected to be launched in Europe next year. Thus, assuring their safety and security is crucial. This paper presents an approach, CESAM&SSM, for modelling safe and secure C-ITS using the CESAM method and the Six-Step Model. A combination of these two methods enables comprehensive analysis of C-ITS from operational, functional, constructional, safety, and security perspectives. The propose approach is compliant with three international standards: ISO 26262 – vehicle safety standard, SAE J3061 – vehicle cybersecurity standard, and ISO 21217 – intelligent transport system architecture standard.

In order to describe the close-in flow field characteristics and the propagation of shock wave of spherical TNT charge bursting in real air, the special features of air under high temperature and high pressure are taken into account and a complete set of dimensionless hydrodynamic equations in Lagrangian distance coordinate system is established by using the state equations of real air. After that, a set of transformed hydrodynamic equations in shock moving coordinates is employed to simplify the numerical calculation. Combining the artificial viscosity technique and shock fitting technique, a method for calculating the whole flow field of TNT bursting in real air is developed. As an example, the calculation results are in good agreement with the existing literature, and experiments data shows that the theory established in this paper is suitable for describing the close-in characteristics and calculating shock wave propagation in distance region of TNT charge bursting in real air.

Autonomous vehicles (AVs) attract a lot of attention recently. They are expected to assist/replace the human drivers in maneuvering the vehicle, thereby reducing the likelihood of road accidents caused by human error, as a means to improve the road traffic safety. However, AVs have their inherent safety and security challenges, which have to be addressed before they are ready for wide adoption. This paper presents an overview of recent research on AV safety failures and security attacks, as well as the available safety and security countermeasures.

Upper Triassic Xujiahe formation locates in the deep west Sichuan depression, where abundant hydrocarbon resources were supplied and geological conditions for reservoir forming were favorable,contains three big structural belts with rich resources:Longmenshan, Xiaoquan-XinChang-Hexinchang-Fenggu, Shiquanchang-Longquanshan, especially the XinChang belonged to Xujiahe Formation of middle depression, the present ground stress has important effect in the exploitation of oil field. Base on the hydraulic fracturing data analyzing, determine the magnitude of horizontal stress as the constraint condition, use the FLAC3D software, and simulate the present ground stress distribution characteristic of second segment of Xujiahe formation, the results show the maximum principal stress direction is mainly east-west, which supply helpful reference to the future exploration and development.

The study takes a field layer as the research object, through numerical simulation technology to establish a theoretical model, select the well pattern which is suited for the synergetic effect of polymer-surfactant flooding and the permeability contrast limits for subdividing layers. On this basis, three sets of well patterns were designed by means of orthogonal design to optimize the factors that affect the surfactant-polymer flooding, including the injection rate, injection-production ratio, slug volume, polymer concentration and surfactant concentration. The results shows that under the different development wells patterns, the pattern adjustment together with the tertiary oil recovery methods to improve oil recovery rate was more than the sum of mere pattern adjustment for water flooding and the compound flooding, reflects the synergetic effect of complex network and tertiary recovery, that is, "Well adjusted + tertiary recovery", this oil extraction model has played a "1 +1>2 "effect.

This paper proposed a novel approach to state-ofcharge (SoC) estimation of the lithium-ion batteries (LiBs) used in electric vehicles (EVs) based on the extended Kalman filtering (EKF). An improved lumped parameter model was developed for describing the dynamic behavior of the LiBs with an optimized open circuit voltage. This improved approach can reduces model error effectively. Other model parameters were identified via the genetic algorithm (GA) to optimizes the polarization time constant. Experimental and simulation results with two kinds of dynamic cycles show that, compared to the commonly used coulomb counting method, the EFK based SoC estimation algorithm is more precise. The proposed methodology can resolve the deficiency of coulomb counting method. The coulomb counting method fails to correct the erroneous initial SoC and is prone to cause greater accumulated error. In contrast, the proposed novel SoC estimation approach can accurately project the SoC trajectory. It employs real-time measurements of battery current and voltage. This approach then can be applied conveniently to battery management system in commercial electric vehicles.