Fracture network types revealed by well test curves for shale reservoirs in the Sichuan Basin,China

Pressure buildup testing can be used to analyze fracture network characteristics and conduct quantitative interpretation of relevant parameters for shale gas wells,thus providing bases for assessing the well productivity and formulating proper development strategies.This study establishes a new well test interpretation model for fractured horizontal wells based on seepage mechanisms of shale reservoirs and proposes a method for identifying fracturing patterns based on the characteristic slopes of pressure buildup curves and curve combination patterns.The pressure buildup curve patterns are identified to represent three types of shale reservoirs in the Sichuan Basin,namely the moderately deep shale reservoirs with high pressure,deep shale reservoirs with ultra-high pressure,and moderately deep shale reservoirs with normal pressure.Based on this,the relationship between the typical pressure buildup curve patterns and the fracture network types are put forward.Fracturing effects of three types of shale gas reservoir are compared and analyzed.The results show that typical flow patterns of shale reservoirs include bilinear flow in primary and secondary fractures,linear flow in secondary fractures,bilinear flow in secondary fractures and matrix,and linear flow in matrix.The fracture network characteristics can be determined using the characteristic slopes of pressure buildup curves and curve combinations.The linear flow in early secondary fractures is increasingly distinct with an increase in primary fracture conductivity.Moreover,the bilinear flow in secondary fractures and matrix and the subsequent linear flow in the matrix occur as the propping and density of secondary fractures increase.The increase in the burial depth,in-situ stress,and stress difference corresponds to a decrease in the propping of primary fractures that expand along different directions in the shale gas wells in the Sichuan Basin.Four pressure buildup curve patterns exist in the Sichuan Basin and its periphery.The pattern of pressure buildup curves of shale reservoirs in the Yongchuan area can be described as 1/2/→1/4,indicating limited stimulated reservoir volume,poorly propped secondary fractures,and the forming of primary fractures that extend only to certain directions.The pressure buildup curves of shale reservoirs in the main block of the Fuling area show a pattern of 1/4/→1/2 or 1/2,indicating greater stimulated reservoir volume,well propped secondary fractures,and the forming of complex fracture networks.The pattern of pressure buildup curves of shale reservoirs in the Pingqiao area is 1/2/→1/4→/1/2,indicating a fracturing effect somewhere between that of the Fuling and Yongchuan areas.For reservoirs with normal pressure,it is difficult to determine fracture network characteristics from pressure buildup curves due to insufficient formation energy and limited liquid drainage.

Heterogeneous Fault-Tolerant Aggregate Signcryption with Equality Test for Vehicular Sensor Networks

The vehicular sensor network (VSN) is an important part of intelligent transportation, which is used for real-timedetection and operation control of vehicles and real-time transmission of data and information. In the environmentofVSN, massive private data generated by vehicles are transmitted in open channels and used by other vehicle users,so it is crucial to maintain high transmission efficiency and high confidentiality of data. To deal with this problem, inthis paper, we propose a heterogeneous fault-tolerant aggregate signcryption scheme with an equality test (HFTASET).The scheme combines fault-tolerant and aggregate signcryption,whichnot onlymakes up for the deficiency oflow security of aggregate signature, but alsomakes up for the deficiency that aggregate signcryption cannot tolerateinvalid signature. The scheme supports one verification pass when all signcryptions are valid, and it supportsunbounded aggregation when the total number of signcryptions grows dynamically. In addition, this schemesupports heterogeneous equality test, and realizes the access control of private data in different cryptographicenvironments, so as to achieve flexibility in the application of our scheme and realize the function of quick searchof plaintext or ciphertext. Then, the security of HFTAS-ET is demonstrated by strict theoretical analysis. Finally, weconduct strict and standardized experimental operation and performance evaluation, which shows that the schemehas better performance.

Temperature field test and prediction using a GA-BP neural network for CRTS Ⅱ slab tracks

The CRTS Ⅱ slab track, which is connected in a longitudinal direction, is one of the main ballastless tracks in China, with approximately 7365 km of operational track. Temperature loading is a very vital factor leading to slab track damages such as warping and cracking. While existing research on temperature distribution rests on either site tests in special environments or theoretical analysis, the long-term temperature field characteristics are not clear. Therefore, a long-term temperature field test for the CRTS Ⅱ slab track on bridge-subgrade transition section was conducted to analyze the temperature field. A GA-BP(genetic algorithm optimized back propagation) neural network was trained on the test data to predict the temperature field. The vertical and lateral temperature distributions in four typical days were carried out. We found that the temperature along the track was distributed in a nonlinear manner. This was particularly distinct in the vertical direction for depths of less than 300 mm. The highest and lowest daily temperatures and the daily range of the temperature were analyzed. With the increasing depth, the daily highest temperatures and range of the temperature were smaller, the daily lowest temperatures were higher, and the time corresponding to this peak value appeared later in the day. Both the highest and lowest daily temperature could be predicted using the GA-BP neural network, though the accuracy in predicting the highest temperature was higher than that in predicting the lowest temperature.

Networked collaborative pseudo-dynamic testing of a multi-span bridge based on NetSLab

Modem dynamic tests such as networked collaborative pseudo-dynamic testing （PDT） provide new tools to study the dynamic performance of large and complex structures. In this paper, several networked collaborative PDT systems established in China and abroad are introduced, including a detailed description of the first networked collaborative platform that involved the construction of a standardized demonstration procedure for networked collaborative PDT. The example is a multi-span bridge with RC piers retrofitted by FRP, and a networked structural laboratory （NetSLab） platform is used to link distributed laboratories located at several universities together. Substructure technology is also used in the testing. The characteristics, resource sharing and collaborative work of NetSLab are described, and the results illustrate that use of the NetSLab is feasible for studying the dynamic performance of multi-span bridge structures.

基于CNN-LSTM混合神经网络的高速铁路地震响应预测

为了更好地挖掘高速铁路在地震时的响应信息,提高光纤光栅监测的效率及预测精度,该文针对地震响应数据的时序性及非线性的特点,提出卷积神经网络(CNN)和长短期记忆(LSTM)网络的混合神经网络模型预测方法.通过在高速铁路简支梁桥上布设准分布式光纤光栅采集地震时轨道板、钢轨、底座板、箱梁的响应数据,在每根光纤上布置7个光栅,利用两边光栅的响应数据预测中间点的光栅响应,将采集位置、历史数据及地震波形等信息作为特征图输入.利用CNN提取特征,再将提前提取出来的特征数据以时序方式作为LSTM网络的输入数据,最后LSTM网络进行地震应变响应预测.实验结果表明,LSTM网络在3层时效果最好,CNN-LSTM方法具有较高的预测精度,根均平方误差(R_(RMSE))、平均绝对误差(R_(MAE))、决定系数(R^(2))分别达到了0.3753、0.2968、0.9371.

基于GA-BP神经网络的大型客机气流角估计方法

为了解决硬件冗余难以克服的气流角传感器共因故障问题,进一步提高飞机气流角信号的可靠性,研究了基于GABP神经网络的气流角估计方法。通过BP神经网络融合姿态角、加速度、风速等参数来实现不依赖气流角传感器的气流角估计;引入遗传算法对神经网络权值和阈值进行全局优化,提高估计精度;对某大型客机的试飞数据预处理后用于模型的训练和测试。仿真结果表明,训练完成的GA-BP神经网络模型对气流角的估计值贴近实际值,稳定性和精度明显高于BP神经网络。上述方法给飞机增加一个余度的气流角信号,可用于传感器故障时为飞机提供可靠的气流角信号。

稳定车-轨道耦合作用下轨排响应特性试验研究

稳定车作为一种大型轨道交通运维装备,对保障铁路线路运行安全至关重要,但是其不具备道床横向阻力的在线感知能力,难以实时智能控制作业参数。为探究稳定车作业时在线感知道床横向阻力的方法,在试验线路上开展轨排动力学试验,测量稳定车-轨道耦合作用下的轨排横向载荷和轨排横向位移,并依据现有标准及现场原位试验法,测量标记轨枕处的道床横向阻力。根据实测的试验数据,采用函数模型拟合道床横向阻力与轨排横向载荷以及轨排横向位移的关系式。并进一步构建径向基函数(Radial Basis Function, RBF)神经网络,采用正交最小二乘学习算法训练网络参数,建立三者之间的非线性模型,对比函数拟合与RBF神经网络模型的准确性。结果表明:道床横向阻力测试中,随着测试轨枕承受横向载荷的增加,轨枕横向位移非线性增大。轨排动力学试验中,轨排横向载荷、轨排横向位移与道床横向阻力之间存在非线性关系。采用函数拟合得到的关系式一定程度上反映了道床横向阻力的非线性变化趋势,但拟合误差较大。采用RBF神经网络建立的模型,在测试集验证中精度更高。道床横向阻力的离线检测方法效率低下,基于RBF神经网络模型的车载感知方法可以提高检测效率。研究成果可为稳定车作业中实时在线感知道床横向阻力及智能控制作业参数提供理论支撑。

基于WPT-CNN的复合绝缘子内部缺陷智能识别研究

超声波技术常用于复合绝缘子内部缺陷的检测,但缺陷识别过程依赖于试验人员专业经验。为实现复合绝缘子内部缺陷的智能识别,提出了一种基于小波包变换和卷积神经网络的超声波检测信号识别模型。首先,通过小波包变换对超声波检测信号进行时频特征提取,并将一维信息转化为二维特征矩阵;其次,将二维特征矩阵输入卷积神经网络中,实现对信号特征的智能识别;最后,采用试验信号样本集对模型进行训练与测试。结果表明,提出的模型能对缺陷、气孔、裂纹、界面脱粘和夹杂五类复合绝缘子超声波检测信号进行识别,且平均准确率可达98.7%,能为复合绝缘子内部缺陷的智能识别提供很好的工程应用参考。

Pulsed Eddy Current Non-destructive Testing and Evaluation:A Review

Pulsed eddy current （PEC） non-destructive test- ing and evaluation （NDT＆E） has been around for some time and it is still attracting extensive attention from researchers around the globe, which can be witnessed through the reports reviewed in this paper. Thanks to its richness of spectral components, various applications of this technique have been proposed and reported in the lit- erature covering both structural integrity inspection and material characterization in various industrial sectors. To support its development and for better understanding of the phenomena around the transient induced eddy currents, attempts for its modelling both analytically and numeri- cally have been made by researchers around the world. This review is an attempt to capture the state-of-the-art development and applications of PEC, especially in the last 15 years and it is not intended to be exhaustive. Future challenges and opportunities for PEC NDT＆E are also presented.

Hybridized Artificial Neural Network for Automated Software Test Oracle

Software testing is the methodology of analyzing the nature of software to test if it works as anticipated so as to boost its reliability and quality.These two characteristics are very critical in the software applications of present times.When testers want to perform scenario evaluations,test oracles are generally employed in the third phase.Upon test case execution and test outcome generation,it is essential to validate the results so as to establish the software behavior’s correctness.By choosing a feasible technique for the test case optimization and prioritization as along with an appropriate assessment of the application,leads to a reduction in the fault detection work with minimal loss of information and would also greatly reduce the cost for clearing up.A hybrid Particle Swarm Optimization(PSO)with Stochastic Diffusion Search(PSO-SDS)based Neural Network,and a hybrid Harmony Search with Stochastic Diffusion Search(HS-SDS)based Neural Network has been proposed in this work.Further to evaluate the performance,it is compared with PSO-SDS based artificial Neural Network(PSO-SDS ANN)and Artificial Neural Network(ANN).The Misclassification of correction output(MCO)of HS-SDS Neural Network is 6.37 for 5 iterations and is well suited for automated testing.

Delay Trigger in the Application of Network Testing System

The researchers who study the local area network( LAN) eXtension for instrumentation( LXI) instrument are pursuing instrument's high-precision synchronization. In the paper,three synchronization modes were discussed which were clock synchronization, trigger synchronization, and response synchronization. Synchronous process between LXI instruments was analyzed and each time factor affecting the synchronization accuracy was discussed. On the basis of the analysis,it can be found that delay trigger plays an important role in the network testing system's synchronization. Delay trigger can produce an additional time interval to correct the difference of each LXI instrument's response time. Then,a method to realize the delay trigger was introduced. Delay time can be adjustable according to the actual demand. Finally,synchronization accuracy of network testing system can reach nanoseconds.

Research on Testing Technologies of Network System in Smart Substations

The network structures of smart substations and the characteristics of industrial Ethernet switches are analyzed.The testing technologies of network systems based on smart substations are specifically elaborated.A viewpoint is proposed that special testing policy&method of smart substation networks should be followed,so that the results can reveal the real network data exchange performance of the whole station.This view ensures the safety and stability of smart substations and lays a foundation for future upgrades and expansions.

Development of a Model Driven ACSI Conformance Testing for IEC 61850-Based IED

Globally, traditional power systems are rapidly transforming towards the adoption of smart grid platforms. Substations which are at the center of the electric power transformation from the power plant are changing to IEC 61850 based digital substations. Therefore, within substation, there is a growing demand for the IEC 61850 based Intelligent Electronic Devices (IEDs). The operation of multiple manufacturers of IEDs in a single digital substation network increases the need for IEC 61850 communications specification conformance diagnosis to ensure interoperability for efficient data exchange between IEDs. The IEC 61850-10 presents test items for diagnosing communication specification conformance. There are many test tools available in the market today to test the compliance of the IEC 61850 communications specifications to the IED. In this paper, we propose a model-based diagnostic method for IED communication conformance testing. The proposed model-based software therefore uses the “drag and drop” technique to select the various IEC 61850 communication services (objects) required to design the test case in a user friendly Graphical User Interface (GUI). This makes the service conformance testing more flexible for test engineers and system integrators especially in situations that require test case modifications. Also, the proposed software tool makes it easy to understand the various IEC 61850 services using the friendly GUI.

基于BP-ANN与RBF-ANN的钢筋与混凝土黏结强度预测模型研究

为研究神经网络对钢筋与混凝土黏结强度的预测能力以及神经网络的输出性能,基于大量的试验数据,提出一种基于改进神经网络的变形钢筋与混凝土黏结强度预测模型,对混凝土结构的研究与实际工程应用均有着重要的意义。收集290组黏结锚固试验数据,引入基于反向传播人工神经网络(BP-ANN)与径向基函数神经网络(RBF-ANN)算法,揭示混凝土强度、保护层厚度、钢筋直径、锚固长度及配箍率对变形钢筋与混凝土黏结性能的影响规律,建立基于改进神经网络算法的钢筋与混凝土黏结强度预测模型。对比分析不同数据预处理方法和训练神经元个数对建议模型预测结果的影响,评估各经典模型与建议模型的预测精度和离散性,提出临界锚固长度计算公式。结果表明:BP-ANN预测值与试验值比值的均值、标准差及变异系数分别为1.009、0.188、0.86,其预测精度略高于RBF-ANN;建议模型能够更准确、更稳定地预测钢筋与混凝土的黏结强度,该方法为解决钢筋与混凝土黏结问题提供了新思路。

Loader and Tester Swarming Drones for Cellular PhoneNetwork Loading and Field Test: Non-stochasticParticle Swarm Optimization

Cellular network operators have problems to test their network without affecting their user experience. Testingnetwork performance in a loaded situation is a challenge for the network operator because network performance differswhen it has more load on the radio access part. Therefore, in this paper, deploying swarming drones is proposed to loadthe cellular network and scan/test the network performance more realistically. Besides, manual swarming dronenavigation is not efficient enough to detect problematic regions. Hence, particle swarm optimization is proposed to bedeployed on swarming drone to find the regions where there are performance issues. Swarming drone communicationshelps to deploy the particle swarm optimization (PSO) method on them. Loading and testing swarm separation help tohave almost non-stochastic received signal level as an objective function. Moreover, there are some situations that morethan one network parameter should be used to find a problematic region in the cellular network. It is also proposed toapply multi-objective PSO to find more multi-parameter network optimization at the same time.

Al/Hf ratio-dependent mechanisms of microstructure and mechanical property of nearly fully dense Al—Hf reactive material

This study proposed three types of Al—Hf reactive materials with particle size ratios(a),which were almost completely dense(porosity of<5.40%)owing to their preparation using hot-pressing technology.Microstructure characteristics and phase composition were analyzed,and the influence of particle size ratios on dynamic mechanical behavior and damage mechanism were investigated.The prepared sample with a=0.1 exhibited continuous wrapping of the Hf phase by the Al phase.Hf—Hf contact(continuous Hf phase)within the sample gradually increased with increasing a,and a small amount of fine Hf appeared for the sample with a=1.The reactive materials exhibited clear strain-rate sensitivity,with flow stressσ0.05and failure strainεfincreasing approximately linearly with increasing strain rate.ε.It is found that the plastic deformation of the material increased with increasing strain rate.As a increased from 0.1 to 1,the flow stress gradually increased.Impact failure of the material was dominated by ductile fracture with a large Al phase plastic deformation band for lower a,while brittle fracture with crushed Hf particles occurred at higher a.Finally,a constitutive model based on BP neural network was proposed to describe the stress-strain relationships of the materials,with an average relative error of 2.22%.

水下控制系统网络可靠性测试方法及在乐东22-1气田的应用

针对水下控制系统网络开展可靠性测试方法研究。首先,分析水下控制系统网络结构,提出一种水下控制系统网络可靠性测试方法,测试网络的连通性及通信质量。其次,从层次结构、通信协议、冗余配置、设备接入网络方式等方面分析水下控制系统网络可靠性。最后,该测试方法成功应用于乐东22-1气田水下控制系统网络通信可靠性测试。

Quantitative Interpretation for the Magnetic Flux Leakage Testing Data Based on Neural Network

In order to interpret the magnetic flux leakage (MFL) testing data quantitatively and size the defects accurately, some defect profiles inversion methods from the MFL signals are studied on the basis of the neural network.Because the wavelet ba- sis function neural network (WBFNN) has good accuracy in the forward calculation and the radial basis function neural network (RBFNN) has reliable precision in the inversion modeling respectively,a new neural network scheme combining WBFNN and RBFNN is presented to solve the nonlinear inversion problem for the MFL data and reconstruct the defect shapes.And such details as the choice of wavelet basis function,the initialization of the weight value and the input normalization are analyzed,the train- ing and testing algorithm for the network are also studied.The inversion results demonstrate that the proposed network scheme has good reliability to interpret the MFL data for some defects.

3D Ice Shape Description Method Based on BLSOM Neural Network

When checking the ice shape calculation software,its accuracy is judged based on the proximity between the calculated ice shape and the typical test ice shape.Therefore,determining the typical test ice shape becomes the key task of the icing wind tunnel tests.In the icing wind tunnel test of the tail wing model of a large amphibious aircraft,in order to obtain accurate typical test ice shape,the Romer Absolute Scanner is used to obtain the 3D point cloud data of the ice shape on the tail wing model.Then,the batch-learning self-organizing map(BLSOM)neural network is used to obtain the 2D average ice shape along the model direction based on the 3D point cloud data of the ice shape,while its tolerance band is calculated using the probabilistic statistical method.The results show that the combination of 2D average ice shape and its tolerance band can represent the 3D characteristics of the test ice shape effectively,which can be used as the typical test ice shape for comparative analysis with the calculated ice shape.

基于相关性检验的VMD-LSTM耦合模型月径流模拟研究

近年来,极端强降雨和干旱事件频发,流域水文过程的不确定性变化加剧,使得流域中长期径流预测的难度增加。为提升LSTM(长短期记忆神经网络)模型对径流时序变化的捕捉及拟合能力,以博阳河流域为研究区域,选取月降雨、蒸发及流量数据,利用VMD(变分模态分解)和相关性检验,排除无关频率分量对LSTM模型规律学习的干扰,以达到模型输入优选的目的;此外,还考虑了VMD与LSTM模型的不同耦合方式对模型精度和稳定性的影响,最终优选出二者兼具的VMD-LSTM月径流耦合模式。结果表明:VMD-LSTM耦合模型可显著提升模拟精度,但在模型稳定性方面有所欠缺;而基于相关性检验的VMD-LSTM耦合模型不仅能够进一步提高模型精度,并且在模型的稳定性方面也有所改进。在基于相关性检验的VMD-LSTM耦合模型的不同耦合方式对比中,对输入、输出均进行VMD分解且对输入变量进行优选的D_(1)耦合方案的模拟效果最好,其60次模拟计算的NSE均为0.98以上且稳定性极佳;另外,在分析方案D_(1)的可解释性时发现历史径流对于LSTM模型的影响要比降雨和蒸发大。该研究结论可为流域水资源管理提供精准可信的中长期径流模拟成果。