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%.

Analysis of sensitivity to hydrate blockage risk in natural gas gathering pipeline

During the operational process of natural gas gathering and transmission pipelines,the formation of hydrates is highly probable,leading to uncontrolled movement and aggregation of hydrates.The continuous migration and accumulation of hydrates further contribute to the obstruction of natural gas pipelines,resulting in production reduction,shutdowns,and pressure build-ups.Consequently,a cascade of risks is prone to occur.To address this issue,this study focuses on the operational process of natural gas gathering and transmission pipelines,where a comprehensive framework is established.This framework includes theoretical models for pipeline temperature distribution,pipeline pressure distribution,multiphase flow within the pipeline,hydrate blockage,and numerical solution methods.By analyzing the influence of inlet temperature,inlet pressure,and terminal pressure on hydrate formation within the pipeline,the sensitivity patterns of hydrate blockage risks are derived.The research indicates that reducing inlet pressure and terminal pressure could lead to a decreased maximum hydrate formation rate,potentially mitigating pipeline blockage during natural gas transportation.Furthermore,an increase in inlet temperature and terminal pressure,and a decrease in inlet pressure,results in a displacement of the most probable location for hydrate blockage towards the terminal station.However,it is crucial to note that operating under low-pressure conditions significantly elevates energy consumption within the gathering system,contradicting the operational goal of energy efficiency and reduction of energy consumption.Consequently,for high-pressure gathering pipelines,measures such as raising the inlet temperature or employing inhibitors,electrical heat tracing,and thermal insulation should be adopted to prevent hydrate formation during natural gas transportation.Moreover,considering abnormal conditions such as gas well production and pipeline network shutdowns,which could potentially trigger hydrate formation,the installation of methanol injection connectors remains necessary to ensure production safety.

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.

Global exhibitors from across textile spectrum gather at upcoming Intertextile Apparel

Shanghai,2 August 2024.Including everything from organic buttons and floral prints,to temperature-regulating and weather-resistant fibres,the apparel value chain is as diverse as it is extensive.Buyers at trade fairs with thousands of exhibitors can be hard pressed finding the fabrics they need,which is a key reason numerous suppliers at autumn's lntertextile Apparel will gather under the banners of specialised zones and pavilions.Taking place from 27-29 August at the National Exhibition and Convention Center (Shanghai),the fair will featu re eight product zones,including Accessories Vision,Functional Lab,and Verve for Design.Meanwhile,suppliers in eight country and region pavilions will showcase various innovations and high-quality textiles.Altogether the platform is set to welcome around 4,000 exhibitors from 25countries and regions.

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.

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。

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.

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。

西南高山峡谷区水土流失综合防治技术与示范

西南高山峡谷区覆盖大横断“七脉六江”地区,是国家“三区四带”重要生态安全屏障,对保障国家生态安全和区域高质量发展具有重要作用。针对西南高山峡谷区存在的水土流失分异演变地带性规律及其关键驱动机制,水土流失对农业耕作、高陡地形、岩溶地质及冻融作用等复杂驱动条件的响应及其防控机制,民族聚集区生态系统服务功能整体提升及生态生产协同的理论与对策等问题,选取兰坪、云龙、永胜、康定以及波密县为典型示范区,开展国家重点研发计划活动,以西南高山峡谷不同类型区水土流失分异规律和综合防治为主线,按照三个层次开展水土流失综合防治与示范研究,攻克高精度土壤侵蚀预报模型与监测技术瓶颈,形成生态系统服务整体提升的“水土流失防治-生态系统服务-民族经济发展”的多目标水土流失综合防治技术体系,最终为西南高山峡谷区生态系统恢复提供科学依据与技术支撑。

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。

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%.

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。

基于轻量化YOLO v8s-GD的自然环境下百香果快速检测模型

为了提高百香果检测精度,并将深度学习模型部署在移动平台上,实现快速实时推理,本文提出一种基于改进YOLO v8s的轻量化百香果检测模型(YOLO v8s-GD)。使用聚集和分发机制(GD)替换颈部特征融合网络,提高模型对百香果图像特征信息跨层融合能力和模型泛化能力;通过基于层自适应幅度的剪枝(LAMP)修剪模型,损失一定精度换取减小模型体积,减少模型参数量,以实现在嵌入式设备上快速检测;运用知识蒸馏学习策略弥补因剪枝而损失的检测精度,提高模型检测性能。实验结果表明,对于自然环境下采集的百香果数据集,改进后模型参数量和内存占用量相比原YOLO v8s基线模型分别降低63.88%和62.10%,精确率(Precision)和平均精度(AP)相较于原模型分别提高0.9、2.3个百分点,优于其他对比模型。在Jetson Nano和Jetson Tx2嵌入式设备上实时检测帧率(FPS)分别为5.78、19.38 f/s,为原模型的1.93、1.24倍。因此,本文提出的改进后模型能够有效检测复杂环境下百香果目标,为实际场景中百香果自动采摘等移动端检测设备部署和应用提供理论和技术支持。

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。

大庆油田能效评价指标体系及能效综合评价方法研究

针对现行油田主要生产系统能效评价指标及其限值和评价方法对于大庆油田不完全适用的问题,根据工作内容,将油田生产系统划分为机采、集输、注水3个子系统;根据用能的关联性,将油田生产系统划分为机采集输和注水两个子系统,对于这两种子系统的划分,分别建立了能效评价指标体系。体系中的能效评价指标均分为系统级、单元级和设备级3个层级;明确了体系中各项指标的定义、计算方法、基础参数来源及统计口径。基于大庆油田实际情况,根据开采区块类型、驱替方式等,将测试数据进行分组,用统计方法确定每组各个能效指标的优良和合格限值;建立了单项与多项组合的油田生产系统加权能效综合评价方法,该方法综合考虑系统效率或系统单耗及其影响因素,在以较大权重计入系统级指标的同时,以较小的权重计入主要影响因素,即在要求系统指标的同时,也对单元级和设备级指标提出要求,从而分析评定所评价系统能效的提升空间。应用此评价方法对6个机采系统、1个集输和1个机采集输系统进行了能效综合评价。

油气企业人才“生聚理用”管理机制

在人才强国战略的指引下,油气企业人力资源管理面临着新形势与新环境,迫切需要破除束缚人才发展的思想观念和制度障碍,健全人才发展管理机制。为此,提出了油气企业人才“生聚理用”的管理机制。研究结果认为:①该机制是在油气企业人才强企战略管理视域下打造油气人才供应链的路径指引和有效保障,协同发挥人才发展边际效益递增效应;②“生才”需通过内生、外生、选拔培养多措并举“使其生”并“使其成长为才”,应持续优化育才规划、拓展人才培养方式、完善培养支撑体系;③“聚才”要在组织适配和岗位匹配前提下发挥人才聚集效应,应多渠道持续引进人才、配套政策吸引人才、服务引领关爱人才;④“理才”要以价值贡献为导向、考核与激励协同,优化全员绩效考核、完善薪酬分配管理、优化人才精准激励奖励;⑤“用才”要在人岗匹配和充分授权的基础上,树立鲜明用人导向、畅通人才职业发展通道、推动人才市场化流动配置。结论认为,通过四个机制协同发展管理,能够为油气企业人才发展管理和人才强企提供有效动能。