Experimental and numerical simulation study on the erosion behavior of the elbow of gathering pipeline in shale gas field

During the production period of shale gas, proppant particles and rock debris are produced together,which will seriously erode the elbows of gathering pipelines. In response to this problem, this paper takes the elbow of the gathering pipeline in the Changning Shale Gas Field as an example to test the erosion rate and material removal mechanism of the test piece at different angles of the elbow through experiments and compares the four erosion models with the experimental results. Through analysis, it is found that the best prediction model for quartz sand-carbon steel erosion is the Oka model. Based on the Oka model, FLUENT software was used to simulate and analyze the law of erosion of the elbow of the gas gathering pipeline under different gas flow velocities, gas gathering pressure, particle size, length of L1,and bending directions of the elbow. And a spiral pipeline structure is proposed to reduce the erosion rate of the elbow under the same working conditions. The results show that this structure can reduce erosion by 34%.

Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.

Slow-gathering areas of rivers, lakes, and reservoirs: Definition, estimation, and restoration

Recently,humans have been facing serious water pollution problems that aggravate water resources shortages.By 2025,two-thirds of the world's population is likely to live in countries with moderate or severe water shortages(Azizullah et al.,2011).Rivers,lakes,and reservoirs are important sources of water.Globally,there are approximately 8.5 million rivers(Linke et al.,2019),21.15 million natural lakes,and 1.427 million large reservoirs(Messager et al.,2016).Many of them have been contaminated due to urbanization,industrialization,and agricultural activities.In particular,all rivers,lakes,and reservoirs have their own slow-gathering areas(SGAs)where the input contaminants are easily stopped and deposited due to their non-open morphologies and low flow speeds.

Extracting angle-domain common-image gathers in VTI media using ansiotropic-Helmholtz P/S wave-mode decomposition

Common-image gathers are extensively used in amplitude versus angle(AVA)and migration velocity analysis(MVA).The current state of methods for anisotropic angle gathers extraction use slant-stack,local Fourier transform or low-rank approximation,which requires much computation.Based on an anisotropic-Helmholtz P/S wave-mode decomposition method,we propose a novel and efficient approach to produce angle-domain common-image gathers(ADCIGs)in the elastic reverse time migration(ERTM)of VTI media.To start with,we derive an anisotropic-Helmholtz decomposition operator from the Christoffel equation in VTI media,and use this operator to derive the decomposed formulations for anisotropic P/S waves.Second,we employ the first-order Taylor expansion to calculate the normalized term of decomposed formulations and obtain the anisotropic-Helmholtz decomposition method,which generates the separated P/S wavefields with correct amplitudes and phases.Third,we develop a novel way that uses the anisotropic-Helmholtz decomposition operator to define the polari-zation angles for anisotropic P/S waves and substitute these angles to decomposing formulations.The polarization angles are then calculated directly from the separated vector P-and S-wavefields and converted to the phase angles.The ADCIGs are thusly produced by applying the phase angles to VTI ERTM.In addition,we develop a concise approximate expression of residual moveout(RMO)for PP-reflections of flat reflectors in VTI media,which avoids the complex transformations between the group angles and the phase angles.The approximate RMO curves show a good agreement with the exact solution and can be used as a tool to assess the migration velocity errors.As demonstrated by two selected examples,our ADCIGs not only produce the correct kinematic responses with regards to different velocity pertubatation,but also generate the reliable amplitude responses versus different angle.The final stacking images of ADCIGs data exhibit the identical imaging effect as that of VTI ERTM.

Deep Learning Based Face Mask Detection in Religious Mass Gathering During COVID-19 Pandemic

Notwithstanding the religious intention of billions of devotees,the religious mass gathering increased major public health concerns since it likely became a huge super spreading event for the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Most attendees ignored preventive measures,namely maintaining physical distance,practising hand hygiene,and wearing facemasks.Wearing a face mask in public areas protects people from spreading COVID-19.Artificial intelligence(AI)based on deep learning(DL)and machine learning(ML)could assist in fighting covid-19 in several ways.This study introduces a new deep learning-based Face Mask Detection in Religious Mass Gathering(DLFMD-RMG)technique during the COVID-19 pandemic.The DLFMD-RMG technique focuses mainly on detecting face masks in a religious mass gathering.To accomplish this,the presented DLFMD-RMG technique undergoes two pre-processing levels:Bilateral Filtering(BF)and Contrast Enhancement.For face detection,the DLFMD-RMG technique uses YOLOv5 with a ResNet-50 detector.In addition,the face detection performance can be improved by the seeker optimization algorithm(SOA)for tuning the hyperparameter of the ResNet-50 module,showing the novelty of the work.At last,the faces with and without masks are classified using the Fuzzy Neural Network(FNN)model.The stimulation study of the DLFMD-RMG algorithm is examined on a benchmark dataset.The results highlighted the remarkable performance of the DLFMD-RMG model algorithm in other recent approaches.

Dataset of Large Gathering Images for Person Identification and Tracking

This paper presents a large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed on the premises of Masjid Al-Nabvi,Madinah,Saudi Arabia.This dataset consists of raw and processed images reflecting a highly challenging and unconstraint environment.The methodology for building the dataset consists of four core phases;that include acquisition of videos,extraction of frames,localization of face regions,and cropping and resizing of detected face regions.The raw images in the dataset consist of a total of 4613 frames obtained fromvideo sequences.The processed images in the dataset consist of the face regions of 250 persons extracted from raw data images to ensure the authenticity of the presented data.The dataset further consists of 8 images corresponding to each of the 250 subjects(persons)for a total of 2000 images.It portrays a highly unconstrained and challenging environment with human faces of varying sizes and pixel quality(resolution).Since the face regions in video sequences are severely degraded due to various unavoidable factors,it can be used as a benchmark to test and evaluate face detection and recognition algorithms for research purposes.We have also gathered and displayed records of the presence of subjects who appear in presented frames;in a temporal context.This can also be used as a temporal benchmark for tracking,finding persons,activity monitoring,and crowd counting in large crowd scenarios.

Study on Influencing Factors and Mechanism of Scientific and Technological Innovation Talents Gathering in Zhejiang Province

This paper systematically studies the influencing factors and mechanism of the agglomeration of scientific and technological innovation talents in Zhejiang Province. The findings are as follows: Firstly, environmental factors such as marketization level, financial development, opening to the outside world, infrastructure construction, medical resources, educational resources, cultural resources and policy support have a positive impact on the agglomeration of scientific and technological talents in Zhejiang Province;Secondly, in the transmission mechanism of the influence of various environmental factors on the concentration of scientific and technological talents in Zhejiang, economic development plays an intermediary role, and the effect is obvious. Various environmental factors indirectly affect the concentration of scientific and technological talents in Zhejiang through economic development. Finally, although both the level and quality of economic development have significant effect on the total concentration of scientific and technological talents in Zhejiang, the mediating effect of economic development level is more significant. These studies are for Zhejiang Province. It provides a theoretical basis for cultivating high-end talents and realizing the agglomeration of scientific and technological innovation talents.

硫化氢环境下老旧管道失效概率评价方法研究

老旧管道在服役过程中存在着失效风险,而影响老旧管道失效的因素有很多,在众多影响因素中硫化氢对管道产生的腐蚀损伤较严重。为解决受腐蚀损伤的老旧管道安全评估问题,文章建立了一种在硫化氢环境下老旧管道失效概率评价方法。以在硫化氢环境中使用了约20年的老旧集气管道为研究对象,采用蒙特卡洛法模拟管道服役过程中的动态压力和腐蚀损伤引起的剩余强度的变化,计算了单一腐蚀因素引起管道失效的概率,并通过正态分布概率给出管道使用的安全范围。研究结果表明:投运20年以上的含硫老旧管道在氢致开裂实验与应力腐蚀开裂实验中,均未发生开裂。根据模型计算得出,老旧管道依旧可以安全服役,但服役6年后失效概率超过0.5,需要进行整修工作,服役27年后,管道内腐蚀严重,剩余强度降到3.82 MPa,需要更换管道。通过模拟得到了失效概率与腐蚀缺陷和服役时间的关系,为老旧集气管道再服役提供理论依据和参考价值。

高分辨率五维地震道集优化处理技术及其应用

五维地震解释技术在裂缝预测和流体识别中发挥了重要作用。道集数据是开展五维地震解释的基础,为了解决五维地震道集存在的各向异性时差、优势频率和信噪比低等问题,提高道集数据质量,提出了高分辨率五维地震道集优化处理技术。通过建立道集去噪、道集提频、时差校正、方位角道集优化以及高精度井震标定定量优化处理参数等技术流程,提高了五维道集数据的分辨率、信噪比和保真度。胜利油田牛庄地区实际OVT资料的应用结果表明,该技术优化处理后同相轴连续性好,地层接触关系更清楚,分辨率明显提高,地震主频由25 Hz提高到43 Hz,优势频带由8~51 Hz拓宽到6~74 Hz;同时,井震相关系数也由0.75提高到0.90。利用处理后的数据精细描述砂体面积43.8 km 2,部署井位3口,实现了该区预测储量升级。实践结果表明,高分辨率五维道集优化处理技术能够充分挖掘五维地震数据的潜力,为后续的叠前反演和储层预测等工作奠定了基础。

第七届世界军人运动会病媒生物防治的经验和教训

大规模集会可能增加媒介疾病传播风险。国外报道大规模集会病媒生物防治经验教训的文献很少。中国通常在大规模集会开展病媒生物防治保障工作投入较多资金及人力用于提升病媒防治水平,以达到控制媒介骚扰和疾病的目标;相关成功经验报道较多,但失误和教训甚少提及。第七届世界军人运动会于2019年10月在武汉举行。军动会有51个竞赛场馆及205个重点保障场所,分布于全市15个城区。蚊媒及其传播疾病防控是最重要的目标之一,武汉成功达到目标,但也有失误和教训。本文总结武汉军运会有害生物防治保障工作中的成功经验,并着重反思其中不足。为提高大规模集会病媒生物防治的效率,建议:①用相关法规推动城市病媒生物防治;②开展应用研究,解决保障工作中遇到的技术及管理问题;③倡导求真务实的工作作风,现场防治工作务求有的放矢;④重视监测质量,制定并严格执行质量控制方案。

GA与分层优化结合的掺水集油工艺参数优化

为降低进入特高含水阶段油田的掺水集油能耗,对掺水集油工艺参数进行优化。以掺水温度、掺水量、掺水压力为外层决策变量,加热炉和掺水泵运行方案为内层决策变量,以掺水集油工艺综合能耗最低为目标函数,建立掺水集油工艺参数优化模型。提出了一种遗传算法(Genetic Algorithm,GA)与分层优化结合的求解策略,将掺水温度作为染色体上的基因,先优化个体对应加热炉和掺水泵运行方案,得到个体对应的最小集油能耗,再通过种群的不断迭代进化,得到掺水集油工艺的最优参数。研究结果表明,与优化前相比,掺水温度降低8.3℃,掺水量减少131.6 m^(3)/d,掺水压力降低0.15 MPa,集输吨液综合能耗降低13.64%,优化效果良好。研究成果可为进入特高含水阶段油田降低掺水集油能耗提供借鉴。

地震应急信息汇聚与发布平台构建

针对震后灾情获取缓慢且碎片化、决策支持不到位、灾情服务缺位等问题,探讨了地震应急信息汇聚与发布平台的设计与构建,运用大数据、云计算技术,汇聚破裂过程、快速评估、辅助决策等各类地震应急产出,实现了各类地震应急信息的自动汇集、全流程模型计算、综合展示等功能,在地震灾情决策支持及地震应急信息服务中发挥了重要作用。

基于多模态神经网络的微地震事件检测

针对微地震有效信号时序特征存在的局限导致微地震事件识别准确率不高的问题,提出了一种基于多模态学习的神经网络微地震事件检测方法。首先,利用道集数据的相关性以目标道为轴对称制作多道时域模态,对目标道进行时频分析得到S域模态特征;然后,联合时域模态和S域模态设计微地震事件检测神经网络,综合多模态的特征进行训练学习,提高微地震事件识别的精度;最后,为验证方法的有效性,对合成微地震信号进行低信噪比数据分析、小幅值数据分析以及实际油井微地震监测信号事件分析。结果表明,该方法可以有效检测低信噪比及微弱的微地震事件;与支持向量机、卷积神经网络、基于监督机器学习方法的对比实验结果表明该方法具有更高的抗噪性与准确率。

“一带一路”倡议下工程性人才集聚环境评价指标体系构建

人才集聚环境是促进人才集聚的原始动力。为了能够科学评价、分析和预测“一带一路”倡议下工程性人才集聚环境存在的优势与不足,基于生态系统理论构建了包括东道国国家环境、工程集群环境和国际工程企业环境的“一带一路”倡议下工程性人才集聚环境评价指标体系,并运用层次分析法计算了工程性人才集聚环境评价指标的权重。运用模糊评判法构建了评估模型,并以“一带一路”沿线国家马来西亚的中国水利国际工程企业为例,使用构建的评价指标体系对其工程性人才集聚环境进行评价。结果表明,2019—2020年马来西亚的中国水利国际工程企业工程性人才集聚环境整体属于“较好”等级,但仍然在文化、生活环境和工程集群环境等方面存在短板。未来,中国水利国际工程企业可以根据东道国国家环境情况采取差异化的项目型人才集聚措施,充分利用工程集群资源,提高自身竞争优势,健全人力资源属地化管理制度。

基于有限元法的油气田集输管网阴极保护布局优化

在利用有限元法建立外加电流阴极保护模型的基础上,借助Sobol灵敏度分析,确定了显著影响某油气田集输管网阴极保护效果的关键参数,并通过粒子群优化(PSO)算法对其系统布局进行优化。结果表明:由于外加电流阴极保护模型的模拟电位与测试桩的测定电位误差较小,故该模型能够较好地反映出油气田集输管网的电位分布现状;改变防护涂层电阻率等一阶敏感性系数较高的因素会对阴极保护效果产生较大影响,土壤电阻率、防护涂层电阻率以及管道埋深与其他参数之间可能存在明显交互作用;当利用PSO算法对油气田集输管网的阴极保护效果进行全局寻优时,该模型经过47700次粒子进化迭代后的最佳阴极保护系统有效覆盖率高达99.14%,优化效果显著。

基于压缩感知的波形指示反演在薄储层预测中的应用

鄂尔多斯盆地东缘大牛地气田二叠系下石盒子组有效砂体致密、规模小、厚度薄,储层非均质性强且地震波阻抗叠置严重。为实现下石盒子组5~6 m致密薄砂岩储层的有效刻画,文中选取了盒1段气藏主力单砂体作为研究对象。在地震正演分析基础上,形成了叠前道集优化提质+压缩感知拓频+纵波标定+波形指示GR反演的技术组合,该技术组合将压缩感知技术与波形指示反演技术的优势有机结合起来,并在盒1段气藏主力单砂体进行了应用。预测结果表明:地震剖面横、纵向分辨率显著提高,其预测成果更加符合地质沉积规律,与实钻井吻合良好,实现了5~6 m致密薄砂岩的有效刻画。该研究为致密砂岩气藏薄储层预测提供了一种新方法。

轮式动态模拟仪在高含水期原油不加热集输中的应用

随着不断深入开发,油田集油管线内产液的流变性逐渐转变为高含水期“水包油”为主的流态,因而管道内的流动条件得以改善。通过前期开展的季节性停掺冷输试验证实,高含水期集输进站温度可以接近凝固点甚至低于凝固点。因此提出利用临界粘壁温度作为采油井不加热集输边界条件,并利用轮式动态模拟分析仪测试单井临界粘壁温度,指导采油井平稳集输,应用后实现措施节气219.6×10^(4)m^(3),节电73.4×10^(4)kWh。

杏北油田掺水系统全流程能量优化方法的研究与应用

油田含水率的上升和产液规模的不断增大,导致集输能耗逐年增加,如何保持开发效益,降低集输能耗,成为高含水开发阶段面临的一大难题。随着节能工作不断深入,潜力已得到很大程度的挖掘,常规技术节能空间小,能耗管控压力大。因此开展能量系统优化技术研究,该方法转变原来区域优化的方式,统筹采油井、计量间、转油站、脱水站四个环节,通过确定末端能耗需求,逐级推导前端各环节能供给的方式,指导集输系统精细化、低能耗生产运行。杏北油田自2017年应用能量优化运行技术,由点及面逐步扩大规模,已实现水驱转油站全覆盖,截至目前累计节气5 621×10^(4)m^(3),节电3 275×10^(4)kWh,为油田集输系统降本增效提供了指导依据。

需求可拆分下生鲜农产品集货路径优化

针对生鲜农产品集货中点多量散、车载限制、货物易腐等特点,研究需求可拆分下生鲜农产品集货的路径优化。通过网点布局与网络需求、车辆路径与载量要求、产品特征与成本消耗等描述生鲜农产品集货基本问题,从制冷、货损、油耗三个方面构造总的集货成本,考虑需求拆分、流量均衡、车辆起始与服务等约束,建立集货路径优化模型;基于遗传算法,以序号排序标定适应度函数,改进轮盘赌选择,用精英策略进行算子分析与遗传操作,设计问题求解模式;最后,以湖南省某乡镇脐橙集货为例进行实证分析。案例分析结果表明:优化后,总集货成本下降19.34%,货损成本减少最多,达1550.86元;集货路径总数减少1条,拆分点由5个变为3个,且按需求进行深度拆分;总行驶里程与时间分别减少72.4 km、2.06 h,整体优化效果明显。研究结果对生鲜农产品集货管理的效率提升有借鉴意义。

B-Spline曲线聚风装置直线翼垂直轴风力机启动性能数值模拟

为了提升直线翼垂直轴风力机的启动性能,基于B-Spline曲线生成方法,提出一种具有流线型轮廓的聚风装置,将其分别安装在风轮顶端和底端,用以提升风轮附近的来流风速,使风轮汲取更多风能,达到更易启动的特点.选取聚风装置的5个结构参数进行设计:聚风装置与风轮间隙距离ΔL、底圆半径R_(1)、顶圆半径R_(2)、入口角度α_(1)和出口角度α_(2).设计方法采用二次旋转正交组合筛选最优模型,通过三维数值模拟研究聚风装置参数对直线翼垂直轴风力机启动性能的影响,获得了最佳外形参数组合.此外,对有无加装聚风装置的直线翼垂直轴风力机进行了静态三维数值模拟.结果表明,通过添加具有外凸流线型轮廓的聚风装置,直线翼垂直轴风力机的启动性能有显著提升.在风速较低的情况下,性能改善更加明显.聚风装置可使直线翼垂直轴风力机的平均启动力矩系数最大增加38.8%,峰值平均启动力矩系数最大可增加31.2%.