Development of degradable pre-formed particle gel(DPPG)as temporary plugging agent for petroleum drilling and production

Temporary plugging agent(TPA)is widely used in many fields of petroleum reservoir drilling and production,such as temporary plugging while drilling and petroleum well stimulation by diverting in acidizing or fracturing operations.The commonly used TPA mainly includes hard particles,fibers,gels,and composite systems.However,current particles have many limitations in applications,such as insufficient plugging strength and slow degradation rate.In this paper,a degradable pre-formed particle gel(DPPG)was developed.Experimental results show that the DPPG has an excellent static swelling effect and self-degradation performance.With a decrease in the concentration of total monomers or cross-linker,the swelling volume of the synthesized DPPG gradually increases.However,the entire self-degradation time gradually decreases.The increase in 2-acrylamide-2-methylpropanesulfonic acid(AMPS)in the DPPG composition can significantly increase its swelling ratio and shorten the self-degradation time.Moreover,DPPG has excellent high-temperature resistance(150°C)and high-salinity resistance(200,000 mg/L NaCl).Core displacement results show that the DPPG has a perfect plugging effect in the porous media(the plugging pressure gradient was as high as 21.12 MPa),and the damage to the formation after degradation is incredibly minor.Therefore,the DPPG can be used as an up-and-coming TPA in oil fields.

Nanoparticle Targeting with Antibodies in the Central Nervous System

Treatments for disease in the central nervous system(CNS)are limited because of difficulties in agent penetration through the blood-brain barrier,achieving optimal dosing,and mitigating off-target effects.The prospect of precision medicine in CNS treatment suggests an opportunity for therapeutic nanotechnology,which offers tunability and adaptability to address specific diseases as well as targetability when combined with antibodies(Abs).Here,we review the strategies to attach Abs to nanoparticles(NPs),including conventional approaches of chemisorption and physisorption as well as attempts to combine irreversible Ab immobilization with controlled orientation.We also summarize trends that have been observed through studies of systemically delivered Ab–NP conjugates in animals.Finally,we discuss the future outlook for Ab–NPs to deliver therapeutics into the CNS.

Degradable preformed particle gel as temporary plugging agent for low-temperature unconventional petroleum reservoirs:Effect of molecular weight of the cross-linking agent

The development of unconventional petroleum resources has gradually become an important succession for increasing oil production.However,the related engineers and researchers are paying more and more attention to the application of temporary plugging agents(TPAs)for their efficient development.TPAs can expand the stimulated reservoir volume(SRV)and facilitate the flow of oil and gas to the bottom of the well.Particle-gels used as temporary plugging agents have the characteristics of the simple injection process,good deformation,high plugging strength,and complete self-degradation performance,which have been widely applied in recent years.In this paper,five samples of DPPG polymerized by different molecular weights of cross-linking agents were prepared.In addition,infrared spectroscopy analysis,differential calorimetry scanning(DSC)analysis,static particle gel swelling and degradation performance evaluation experiments,and dynamic temporary plugging performance experiments in cores were conducted at 34°C.Results show that as the molecular weight of the cross-linking agent(at 0.01 g)in the DPPG molecule decreased from 1,000 to 200 Da,the fewer cross-linking sites of DPPG,the looser the microscopic three-dimensional mesh structure formed.The swelling ratio increased from 7 to 33 times.However,the complete degradation time increased from 40 to 210 min.Moreover,the DSC results confirmed that the higher the molecular weight of the cross-linking agent,the worse is chemical stability and the more prone it to self-degradation.DPPG samples had good temporary plugging performance in reservoir cores.DPPGs prepared by the cross-linking agent with smaller molecular weight has a stronger swelling ratio,higher gel strength,and greater plugging strength in the core permeabilities.Moreover,the degraded DPPG is less damaging to the cores.However,their slower degradation rates take a slightly longer times to reach complete degradation.The results of this paper can provide new ideas and a theoretical basis for the development of particle gel-type temporary plugging agents(TPA)with controllable degradation time in low-temperature reservoirs.It can help to expand the application range of existing DPPG reservoir conditions.

Multi-objective Optimization of Multi-Agent Elevator Group Control System Based on Real-time Particle Swarm Optimization Algorithm

In order to get a globally optimized solution for the Elevator Group Control System (EGCS) scheduling problem, an algorithm with an overall optimization function is needed. In this study, Real-time Particle Swarm Optimization (RPSO) is proposed to find an optimal solution to the EGCS scheduling problem. Different traffic patterns and controller mechanisms for EGCS are analyzed. This study focuses on up-peak traffic because of its critical importance to modern office buildings. Simulation results show that EGCS based on Multi-Agent Systems (MAS) using RPSO gives good results for up-peak EGCS scheduling problem. Besides, the elevator real-time scheduling and reallocation functions are realized based on RPSO in case new information is available or the elevator becomes busy because it is unavailable or full. This study contributes a new scheduling algorithm for EGCS, and expands the application of PSO.

Nanoparticle foaming agents for major gas fields in China

The conventional foaming agents have the problems of poor adaptability and high cost during the application in different types of gas fields,especially in high temperature,high salinity,high acidic gas and high condensate oil and gas fields.In this study,the Gemini foaming agent was used as the main agent to enhance foaming and foam stability of the foaming agent,the grafted nanoparticles were used as foam stabilizer to further improve the foam stability,and the characteristic auxiliaries were added to make the foaming agent suitable for different types of gas reservoirs.Two types and six subtypes of nanoparticle foaming agents have been prepared for the main gas fields of China.The experimental evaluation results show that the overall temperature resistance,salinity resistance,H2S resistance,CO2 resistance and condensate resistance of the nanoparticle foaming agents can reach 160℃,250000 mg/L,100 mg/L,100%and 40%,respectively.The new foaming agents have been used in 8685 wells in China.Compared with conventional foaming agent,the average gas flow rate per well increased by 62.48%,the pressure difference(casing-tubing)decreased by 18.9%,and the cost dropped by 45%.The effect of reducing cost and increasing efficiency is obvious.

A Multi-Agent Particle Swarm Optimization for Power System Economic Load Dispatch

A new versatile optimization, the particle swarm optimization based on multi-agent system (MAPSO) is presented. The economic load dispatch (ELD) problem of power system can be solved by the algorithm. By competing and cooperating with the randomly selected neighbors, and adjusting its global searching ability and local exploring ability, this algorithm achieves the goal of high convergence precision and speed. To verify the effectiveness of the proposed algorithm, this algorithm is tested by three different ELD cases, including 3, 13 and 40 units IEEE cases, and the experiment results are compared with those tested by other intelligent algorithms in the same cases. The compared results show that feasible solutions can be reached effectively, local optima can be avoided and faster solution can be applied with the proposed algorithm, the algorithm for ELD problem is versatile and efficient.

火山渣粒径和DPS掺量对轻骨料混凝土性能的影响

以火山渣作为粗骨料制备了轻骨料混凝土,研究了火山渣粒径(4.75~9.50、4.75~19.00、4.75~26.00 mm)对轻骨料混凝土坍落度、吸水率和抗压强度的影响,优选出了最佳火山渣粒径。在此基础上,研究了渗透结晶型无机防水剂(DPS)的掺量(0、0.6%、0.8%、1.0%)对轻骨料混凝土性能的影响,并分析了其作用机理。结果表明:随着火山渣粒径的增大,轻骨料混凝土的坍落度降低,吸水率增大,抗压强度先增大后减小,最佳火山渣粒径为4.75~19.00 mm;随着DPS掺量的增加,轻骨料混凝土的坍落度和吸水率降低,抗压强度增大;掺入适量DPS可以有效提高混凝土基体的密实度,从而改善混凝土的性能。

工业副产石膏制备高强石膏的方法及其应用技术

本文论述了高强石膏的制备方法,从高强石膏传统的制备工艺入手,分析了蒸压法(饱和蒸汽法)、常压盐/酸溶液法和加压盐/酸溶液法的优点及缺点,阐释了在高强石膏的制备过程中转晶剂的应用以及优化颗粒级配对提高α-半水石膏性能的重要性,论述了高强石膏的技术要求。在建材、模具铸造、航空航天、医疗以及美术制品等领域,高强石膏有非常广泛的应用,介绍了其出众的性能,最后讨论了水膏比对高强石膏硬化浆体强度的影响,以及高强石膏制品耐水性差的原因和解决方法。

基于多目标粒子群优化算法的某轻型商用车操纵稳定性优化研究

针对某轻型商用车稳态回转时侧倾度偏大的问题对其悬架进行优化改进。基于ADAMS/car搭建整车多体动力学模型,通过前悬架反向平行轮跳试验、后悬架理论计算验证了悬架仿真模型的准确性。进行整车稳态回转工况和转向盘中间位置转向工况仿真分析,结果表明,车身侧倾度偏高。为实现操纵稳定性优化分析的流程自动化,提出了基于modeFRONTIER的联合仿真方法。以悬架设计参数为优化变量,以汽车的侧倾度与横摆角速度响应滞后时间为优化目标,采用拉丁超立方试验设计方法拟合得到混合代理模型,并结合多目标粒子群优化算法对悬架系统进行多目标优化,获得了悬架系统优化方案。优化结果显示,在不影响平顺性的前提下,汽车车身侧倾度降低了13.93%,横摆角速度响应滞后时间降低了2.75%,整车操纵稳定性得到了提升。

湿润剂对育苗基质水分吸持和黄瓜穴盘苗生长的影响

基质水分吸持特性直接影响基质含水量和蔬菜穴盘苗生长发育。为了探明湿润剂对穴盘育苗基质水分吸持的作用,研究了湿润剂添加后对不同粒径育苗基质的水分吸收量、蒸散速率和黄瓜穴盘苗生长的影响,结果表明:小粒径基质吸水速率和吸水量显著低于大粒径基质,湿润剂更有利于增加小粒径基质吸水量,相对于大粒径基质增加了17.32百分点;添加湿润剂同时提高了穴盘育苗基质吸水速率和蒸散速率,并使基质在1、2、3次吸水-蒸散过程始终保持较高相对含水量;基质添加湿润剂可有效促进小粒径基质条件下黄瓜穴盘苗生长发育,显著提高了黄瓜幼苗根冠比和成坨率。表明基质添加湿润剂可有效改善小粒径育苗基质水分吸持特性,有利于黄瓜穴盘苗壮苗形成。

元素硫在溶硫剂中扩散溶解行为的分子动力学模拟

溶硫解堵是高含硫气藏开发中处理硫沉积堵塞的常用方法。建立硫分子晶体形貌,采取分子动力学方法计算了溶硫主剂(二甲基二硫醚DMDS、CS_(2)、二甲亚砜DMSO、环己胺CHA)与硫晶体界面的吸附能,以及S8分子在扩散溶解过程中的径向分布函数、均方位移及扩散系数,研究了溶硫主剂的扩散溶硫行为。结果表明:晶体直接生长方法(AE)得到的S8分子晶体形貌接近实际沉积的元素硫颗粒,硫与溶硫主剂的吸附能在-612~-1 479 kJ·mol^(-1),产生较强的相互作用;溶硫主剂分子与硫分子构成的溶硫盒子中,溶硫主剂分子与S_(8)分子产生强烈的范德华力,促进扩散溶硫,其中CS_(2)与S_(8)分子间在距离0.26 nm处相互作用最强,产生物理溶硫;DMDS、DMSO、CHA在距离0.23 nm处产生氢键作用,化学与物理作用协同溶硫。

多Agent结构的混沌PSO在无功优化中的应用

针对电力系统中,优化问题具有高维、多约束、非线性特点,提出一种基于多Agent体系结构的混沌粒子群优化算法。在分析粒子群算法的基础上,融合多Agent技术与混沌局部搜索算法,将粒子群算法的群搜索特征和Agent的智能搜索特征相结合。算法利用环境信息与周围Agent进行竞争合作,提升全局探索能力,增强信息多样性;同时依靠混沌局部搜索的无重复遍历性摆脱局部极值,提高算法在解空间中的收敛精确度。为验证算法有效性,对典型多约束、非连续的电力系统无功优化问题进行建模仿真。IEEE 30节点和118节点系统仿真计算的结果表明,与其他优化算法在收敛特性,最优解,鲁棒性等方面相比,该算法对解决电力系统无功优化问题是可行和有效的。

免疫Agent和量子粒子群优化的入侵检测方法研究

针对传统入侵检测系统检测速度慢和误检率高的问题,将免疫原理、移动Agent技术和量子粒子群优化算法相结合,提出了基于免疫Agent和粒子群优化算法的入侵检测模型。介绍了系统模型与体系结构,并对系统性能进行仿真实验。实验结果对比表明,系统能提高传统入侵检测系统的检测速度和降低误检率。

基于QPSO的单任务Agent联盟形成

智能群体搜索算法在求解单任务Agent联盟时稳定性较差、收敛速度慢、全局寻优能力不强,因此采用优化的量子粒子群优化算法解决上述问题。利用群体历史优质解,在最优粒子变异的基础上,采用多种群并行搜索,防止陷入局部极值,并对粒子群进行筛选以加快粒子群的收敛速度。对比实验结果表明,该算法可以快速、高效地找出合适的Agent联盟,在运行时间和解的质量方面优于同类算法。

多Agent主从粒子群分布式计算框架

面向大规模复杂优化问题,提出了一个基于并行粒子群优化的分布式Agent计算框架.框架中使用一个主群(master swarm)来演化问题的完整解,并使用一组从群(slave swarm)来并行优化一组子问题的解,主群和从群通过交替执行来提高问题的求解效率.采用异步组结构,主群/从群中的各类Agent共享一个解群,并通过相互协作,对解群进行构造、改进、修补、分解和合并等演化操作.该框架可用于求解复杂的约束多目标优化问题.通过一类典型运输问题上的实验,其结果表明,所提出的方法明显优于另外两种先进的演化算法.

基于多Agent的风力/太阳能互补发电场能量管理系统

为提高互补发电场能量管理的合理性,提出采用多Agent技术实现其能量的分布式管理。针对一个独立供电的互补发电场,设计该发电场能量管理系统的组织结构,构造其能量管理Agent的理论模型及各组成模块。在该系统中,Agent采用非固定主从式合作机制,并根据当前采样时刻的最大出力确定Agent之间的主从关系。讨论了能量管理系统中多Agent的协调和协作机制,在采用合同网合作方法的基础上引入市场竞争,通过招、投标确定有调节能力的从Agent。将微粒群算法作为Agent的决策算法,用以确定中标Agent的最佳调节序列,实现母线电压越限时的能量优化管理。最后,采用数字仿真证明了该能量管理系统的可行性。

基于多Agent粒子群优化算法的电力系统经济负荷分配

为更好地解决电力系统中经济负荷分配非连续、非凸、非线性的优化问题,提出一种新的基于多Agent系统竞争与合作机制的多Agent粒子群优化算法。该算法以粒子群算法和智能体技术为基础,将粒子群算法的群搜索特征和智能体的智能搜索特征相结合,使粒子在搜索过程中利用群体信息,环境信息共同决定搜索策略。每一个Agent相当于粒子群优化算法中的一个粒子,通过与其随机选取的邻居的竞争、合作操作,能够充分获得问题信息并自适应地权衡粒子的全局探索能力和局部开发能力,可更快、更精确地收敛到全局最优解。对IEEE3节点,IEEE13节点和IEEE40节点系统的仿真结果表明,该算法在求取电力系统ELD问题时具有较高质量的解和运行速度。

一种非线性动态自适应的Agent联盟生产算法

代理(Agent)联盟是对无线传感器网络WSN(Wireless Sensor Network)进行管理的重要手段.引入粒子群算法PSO(Particle Swarm Optimization)并对其进行改进,使PSO的参数具有非线性动态自适应性.将改进的PSO用于求解Agent联盟生成问题,并针对WSN的特性设计了一种效益函数用于评价联盟的效益.采用基于非线性动态自适应PSO的Agent联盟生成算法,在联盟生成初期搜索范围较广,搜索后期在局部挖掘上表现出良好的性能.实验证明在解决Agent联盟生成问题中,基于PSO的算法在稳定性上优于其他算法,基于改进PSO的联盟生成算法可以加大搜索空间,更快的收敛到最优解,且该算法可以同时生成多个Agent联盟,支持并行多任务环境下的Agent联盟求解.

无线传感器网络移动Agent路由算法的研究与仿真

提出一种新的基于虚拟连通拓扑子图的优化模型用于无线传感器网络移动Agent路由。建立包含有效节点的虚拟连通拓扑子图,将Agent访问路径分解为采集子路径和返回子路径,提出一种基于GA与离散PSO的混合算法GAPSO用于求解该问题。该算法可大大降低解空间复杂度,提高搜索效率。仿真结果表明随着网络规模增大,该优化模型优势明显,GAPSO表现出比GA更好的性能。

基于多Agent的虚拟企业任务调度模型及优化

采用多智能体技术构建虚拟企业任务调度模型,并对基于该模型的任务调度运作过程进行说明.针对调度优化问题,以资源智能体承担的生产任务为研究对象,综合考虑生产任务之间的时序逻辑关系、作业时间及资源自身已确定的生产任务等影响因素,建立以生产延续时间最小为目标的优化模型,给出粒子群优化求解算法.应用实例及数字仿真验证了模型及优化算法有效性。