Wettability alteration and oil recovery by spontaneous imbibition of smart water and surfactants into carbonates

Naturally fractured carbonate reservoirs have very low oil recovery efficiency owing to their wettability and tightness of matrix.However,smart water can enhance oil recovery by changing the wettability of the carbonate rock surface from oilwet to water-wet,and the addition of surfactants can also change surface wettability.In the present study,the effects of a solution of modified seawater with some surfactants,namely C12 TAB,SDS,and TritonX-100(TX-100),on the wettability of carbonate rock were investigated through contact angle measurements.Oil recovery was studied using spontaneous imbibition tests at 25,70,and 90°C,followed by thermal gravity analysis to measure the amount of adsorbed material on the carbonate surface.The results indicated that Ca2+,Mg2+,and SO42-.ions may alter the carbonate rock wettability from oil-wet to water-wet,with further water wettability obtained at higher concentrations of the ions in modified seawater.Removal of NaCl from the imbibing fluid resulted in a reduced contact angle and significantly enhanced oil recovery.Low oil recoveries were obtained with modified seawater at 25 and 70°C,but once the temperature was increased to 90°C,the oil recovery in the spontaneous imbibition experiment increased dramatically.Application of smart water with C12 TAB surfactant at 0.1 wt%changed the contact angle from 161°to 52°and enhanced oil recovery to 72%,while the presence of the anionic surfactant SDS at 0.1 wt%in the smart water increased oil recovery to 64.5%.The TGA analysis results indicated that the adsorbed materials on the carbonate surface were minimal for the solution containing seawater with C12 TAB at 0.1 wt%(SW+CTAB(0.1 wt%)).Based on the experimental results,a mechanism was proposed for wettability alteration of carbonate rocks using smart water with SDS and C12 TAB surfactants.

Wettability alteration analysis of smart water/novel functionalized nanocomposites for enhanced oil recovery

Smart water flooding,as a popular method to change the wettability of carbonate rocks,is one of the interesting and challenging issues in reservoir engineering.In addition,the recent studies show that nanoparticles have a great potential for application in EOR processes.However,little research has been conducted on the use of smart water with nanoparticles in enhanced oil recovery.In this study,stability,contact angle and IFT measurements and multi-step core flooding tests were designed to investigate the effect of the ionic composition of smart water containing SO4^2- and Ca^2+ ions in the presence of nanofluid on EOR processes.The amine/organosiloxane@Al2O3/SiO2(AOAS) nanocomposite previously synthesized using co-precipitation-hydrothermal method has been used here.However,for the first time the application of this nanocomposite along with smart water has been studied in this research.Results show that by increasing the concentrations of calcium and sulfate ions in smart water,oil recovery is improved by 9% and 10%,respectively,compared to seawater.In addition,the use of smart water and nanofluids simultaneously is very effective on increasing oil recovery.Finally,the best performance was observed in smart water containing two times of sulfate ions concentration(SW2 S) with nanofluids,showing increased efficiency of about 7.5%.

Effects of salinity and ionic composition of smart water on mineral scaling in carbonate reservoirs during water flooding

This work was conducted to study the risk of formation damage as the result of mineral scales deposition during smart waterflooding into carbonate core sample,as well as the influence of injected water salinity and ionic composition on mineral scaling and precipitation.The reservoir flowing conditions were simulated by a new laboratory core-flooding procedure,which took into count of the effect of in-situ contact time(CT)of injected water and formation water on scaling.After the optimum CT was determined,extent of permeability decline was studied by the change in the salinity and ionic composition of injection seawater.The scaled core sample was analyzed visually by scanning electron microscopy(SEM)to study the crystal morphology of the scale.Under the experimental conditions,extent of permeability decline caused by CaSO_(4) and CaSO_(3) composite scales ranged from 61% to 79.1% of the initial permeability.The salinity and the ionic composition of injected smart water,and CT of the mixing waters had significant effects on the co-precipitation of CaSO_(4) and CaSO_(3) scales.The SEM images reveal that the loss of permeability is mainly caused by the accumulation and growth perpendicular to the pore wall of scale crystals.

Augmented IoT Model for Smart Agriculture and Farm Irrigation Water Conservation

In Northern Nigeria, irrigation systems are operated manually. Agriculture has over the years been practiced primitively by farmers, especially in sub-Saharan Africa. This is due to the absence of intelligent technological know-how where its practice could be leveraged upon. Agricultural practice is constrained by some major challenges ranging from traditional way of farming, understating of concepts, practices, policy, environmental and financial factors. The aim of this study was to optimize an IoT-based model for smart agriculture and irrigation water management. The objectives of the study were to: design, implement, test and evaluate the performance of the optimized IoT-based model for smart agriculture and irrigation water management. The method used in the study was the prototyping model. The system was designed using balsamiq application tools. The system has a login page, dashboard, system USE-CASE diagrams, actuators page, sensor page and application interface design. Justinmind tool was used to show the flow of information in the system, which included data input and output, data stores and all the sub-processes the data moves through. The Optimized IoT model was implemented using four core platforms namely, ReactJS Frontend Application development platform, Amazon web services IoT Core backend, Arduino Development platform for developing sensor nodes and Python programming language for the actuator node based on Raspberry Pi board. When compared with the existing system, the results show that the optimized system is better than the existing system in accuracy of measurement, irrigation water management, operation node, platform access, real-time video, user friendly and efficiency. The study successfully optimized an IoT-based model for smart agriculture and irrigation water management. The study introduced the modern way of irrigation farming in the 21st century against the traditional or primitive way of irrigation farming that involved intensive human participation.

Enhancing the spontaneous imbibition rate of water in oil-wet dolomite rocks through boosting a wettability alteration process using carbonated smart brines

Most fractured carbonate oil reservoirs have oil-wet rocks.Therefore,the process of imbibing water from the fractures into the matrix is usually poor or basically does not exist due to negative capillary pressure.To achieve appropriate ultimate oil recovery in these reservoirs,a water-based enhanced oil recovery method must be capable of altering the wettability of matrix blocks.Previous studies showed that carbonated water can alter wettability of carbonate oil-wet rocks toward less oil-wet or neutral wettability conditions,but the degree of modification is not high enough to allow water to imbibe spontaneously into the matrix blocks at an effective rate.In this study,we manipulated carbonated brine chemistry to enhance its wettability alteration features and hence to improve water imbibition rate and ultimate oil recovery upon spontaneous imbibition in dolomite rocks.First,the contact angle and interfacial tension(IFT)of brine/crude oil systems were measured for several synthetic brine samples with different compositions.Thereafter,two solutions with a significant difference in WAI(wettability alteration index)but approximately equal brine/oil IFT were chosen for spontaneous imbibition experiments.In the next step,spontaneous imbibition experiments at ambient and high pressures were conducted to evaluate the ability of carbonated smart water in enhancing the spontaneous imbibition rate and ultimate oil recovery in dolomite rocks.Experimental results showed that an appropriate adjustment of the imbibition brine(i.e.,carbonated smart water)chemistry improves imbibition rate of carbonated water in oil-wet dolomite rocks as well as the ultimate oil recovery.

Automatic Fault Diagnosis of Smart Water Meter Based on BP Neural Network

The smart water meter in water supply network can directly affect water production and usage when faults occur.The traditional method of fault detection is inefficient with time lagging,which is not helpful for modernization of water supply system.The capability of automatic fault diagnosis of smart water meter is an important means to improve the service quality of water supply.In this paper,an automatic fault diagnosis method for the smart device is proposed based on BP neural network.And it was applied on Google Tensorflow platform.Fault symptom vectors were constructed using water meter status data and were used to train the neural network model.In order to improve the learning convergence speed and fault classification effect of the network,a method of weighted symptom was also employed.Experimental results show that it has good performance with a general fault diagnosis accuracy of 98.82%.

Smart Irrigation System: A Water Management Procedure

This paper presents a smart irrigation system suitable for use in places where water scarcity is a challenge. In many parts of Africa, even when irrigation is practiced, it is manually operated. Smart irrigation system is thereby believed to be a major solution. The paper therefore presents a smart irrigation system that optimizes the available water in the water reservoir thus providing an efficient and effective water usage solution for the irrigation system. The irrigation system is able to automatically start/stop water pumps on the irrigation site based on the soil moisture content acquired from the moisture content sensor as well as the ultrasonic sensor measuring the water level in the reservoir. The measured sensor values are sent to the Arduino microcontroller for configuring the control algorithm. The system prioritizes irrigation operation by determining the number of pumps to be operated at any instance as well as their locations. In this way, different crops can be watered depending on their varying water requirements. In order to implement the design, a laboratory scale architectural model depicting a farm setting with reservoir, direct current (DC) pumps and the control unit was constructed. Experimental results revealed good performance which makes the developed system a suitable tool for studies on irrigation.

A Preliminary Investigation of Smart Rural Water Distribution Systems in the Gambia

An estimated one-third of water points in rural sub-Saharan Africa are non-functioning at any one time because of lack of upkeep. Communities are left without access to clean drinking water and this has multiple knock-on developmental impacts. An innovative pre-payment and Internet-of-Things enabled “e-Tap” based water technology and management system cycles revenue back into operation and maintenance and collects accurate and real-time data on consumption and tap failures. This has been operational in the Gambia since April 2016. Preliminary research has begun on evaluating this innovation. Technical tests were conducted to examine the efficiency of the e-Tap under varying conditions. Water use trends were then analysed by using the cloud-collected data transmitted from operational e-Taps. Further, baseline sveys to investigate social parameters were undertaken on 20 user households. This exploratory research shows the e-Taps to work efficiently in the laboratory and the Gambia with negligible failures, and to reduce distances users must travel for clean water and time they spend collecting.

Protecting Water Quality and Public Health Using a Smart Grid

After the attacks on September 11, 2001 and the follow-up risk assessments by utilities across the United States, securing the water distribution system against malevolent attack became a strategic goal for the U.S. Environmental Protection Agency. Following 3 years of development work on a Contamination Warning System (CWS) at the Greater Cincinnati Water Works, four major cities across the United States were selected to enhance the CWS development conducted by the USEPA. One of the major efforts undertaken was to develop a process to seamlessly process “Big Data” sets in real time from different sources (online water quality monitoring, consumer complaints, enhanced security, public health surveillance, and sampling and analysis) and graphically display actionable information for operators to evaluate and respond to appropriately. The most significant finding that arose from the development and implementation of the “dashboard” were the dual benefits observed by all four utilities: the ability to enhance their operations and improve the regulatory compliance of their water distribution systems. Challenge: While most of the utilities had systems in place for SCADA, Work Order Management, Laboratory Management, 311 Call Center Management, Hydraulic Models, Public Health Monitoring, and GIS, these systems were not integrated, resulting in duplicate data entry, which made it difficult to trace back to a “single source of truth.” Each one of these data sources can produce a wealth of raw data. For most utilities, very little of this data is being translated into actionable information as utilities cannot overwhelm their staffs with manually processing the mountains of data generated. Instead, utilities prefer to provide their staffs with actionable information that is easily understood and provides the basis for rapid decision-making. Smart grid systems were developed so utilities can essentially find the actionable needle in the haystack of data. Utilities can then focus on rapidly evaluating the new information, compare it known activities occurring in the system, and identify the correct level of response required. Solution: CH2M HILL was engaged to design, implement, integrate, and deploy a unified spatial dashboard/smart grid system. This system included the processes, technology, automation, and governance necessary to link together the disparate systems in real time and fuse these data streams to the GIS. The overall solution mapped the business process involved with the data collection, the information flow requirements, and the system and application requirements. With these fundamentals defined, system integration was implemented to ensure that the individual systems worked together, eliminating need for duplicate data entry and manual processing. The spatial dashboard was developed on top of the integration platform, allowing the underlying component data streams to be visualized in a spatial setting. Result: With the smart grid system in place, the utilities had a straightforward method to determine the true operating conditions of their systems in real time, quickly identify a potential non-compliance problem in the early stages, and improve system security. The smart grid system has freed staff to focus on improving water quality through the automation of many mundane daily tasks. The system also plays an integral role in monitoring and optimizing the utilities’ daily operations and has been relied on during recovery operations, such as those in response to recent Superstorm Sandy. CH2M HILL is starting to identify the processes needed to expand the application of the smart grid system to include real-time water demands using AMI/AMR and real-time energy loads from pumping facilities. Once the smart grid system has been expanded to include Quality-Quantity-Energy, CH2M HILL can apply optimization engines to provide utility operations staffs with a true optimization tool for their water systems.

智慧水利理论体系与数字孪生流域虚拟模型研究成果述要

智慧水利是水利高质量发展的显著标志之一。围绕智慧水利理论体系不完善、流域映射精准度不高、模型精度不高、决策支撑程度低等问题,建立了由智慧流域基础理论、数字孪生流域基础理论和智能水网理论框架组成的智慧水利理论体系,研究了由数字孪生感知节点模型、数字孪生降水遥感模型、数字孪生蒸散发遥感模型、数字孪生土壤水遥感模型、数字孪生水体遥感模型、数字孪生地下水遥感模型、数字孪生水质遥感模型组成的数字孪生流域感知模型,构建了由数字孪生流域水文模型、数字孪生水动力水质模型、数字孪生动态调控模型、数字孪生虚拟仿真模型、数字孪生协同决策模型、数字孪生并行组合模型等组成的数字孪生流域虚拟模型。研究成果可为我国智慧水利建设提供理论基础和技术支撑。

实施水资源刚性约束制度的智慧化管理需求探讨

建立水资源刚性约束制度是提升水资源节约集约利用水平的重要举措,实施水资源刚性约束制度需要加强数字赋能。从水资源刚性约束对象与内容、水资源刚性约束指标及目标、水资源刚性约束措施等方面,分析了实施水资源刚性约束制度的智慧化管理业务需求,提出建立由实体水资源、数字水资源、市场水资源构成的实施水资源刚性约束制度的智慧化管理体系,分析了智慧化管理体系赋能水资源刚性约束制度实施的路径。

城市内涝预警预报系统研发及应用

城市内涝问题具有成因多元、影响面广的特点,是基础性社会问题。分析了城市内涝的成因,提出通过信息化手段提升城市内涝灾害应对能力。构建基于内涝实时物联感知、数据汇聚、数学模型预报、大数据分析、水利数字孪生的城市内涝预警预报系统,并在宁波市进行了应用。通过搭建“天基”监测预警、“落地雨”监测预警、内涝预报预警、内涝成因分析、内涝影响评估等功能模块,实现了城区内涝的精准模拟、风险图的在线绘制以及内涝损失的高效评估,有力支撑了城市暴雨内涝灾害预报预警,提升了城市内涝风险管控能力。

河南郑州市防洪“五预”系统建设及应用

当前大数据、云计算、数字孪生等新兴技术在防洪工作中的应用,极大提高了洪水预报预警能力,为新时代防汛减灾提供了新的思路。为响应河南省政府防洪“五预”(预报、预判、预警、预案、预演)措施建设的号召,落实郑州市委、市政府“7·20”特大暴雨灾害调查报告整改和灾后重建工作专题会上的指示精神,针对郑州市河流众多、水系复杂的特点,开展郑州市防洪“五预”系统建设。基于“五预”理念,从监测感知和支撑平台方面梳理了郑州市防洪“五预”系统的总体架构,介绍了防洪预报、预判、预警、预演、预案“五预”模块的主要功能。该系统的建成对提高郑州市灾害预防和预警能力、增强郑州市对突发性洪水灾害的反应能力、有效降低灾害损失、保障社会经济正常运行和社会安定有重要意义,也为大型城市智慧水利建设提供了参考和借鉴。

城市洪涝治理与韧性城市建设:变革、创新与启示

我国城市洪涝治理经历了从工程措施到工程与非工程措施并举,从洪涝分治到洪涝统筹,从城市局部治理到流域系统治理的重大转变。特别是海绵城市和韧性城市理念的引入,显著提升了城市洪涝治理水平,促进了城市可持续发展。经过多年探索与实践,我国在技术应用、政策管理、公众与社会参与等方面取得了显著进展,城市洪涝模拟与风险评估取得重要成果,监测感知系统日趋完善,预报预警技术取得突破,应用场景更加广泛;跨部门协作与联动机制、政府与社会资本合作模式的提出,高效信息传播机制和公众参与程度的加强,均显著增强了城市应对洪涝灾害的能力,为防洪减灾工作提供了有力支撑。借助北斗卫星、激光雷达、物联网、人工智能、大数据等先进技术,我国持续推进数字孪生流域、国家水网与区域水网建设,为洪涝治理和水利高质量发展提供了重要支撑。今后,城市洪涝治理将继续朝着系统化、智能化、生态化方向发展,面对新时代的机遇与挑战,应结合海绵城市与韧性城市建设,坚持系统治理、坚守底线思维、坚持创新发展,通过全局性谋划和整体性推动,不断提升水旱灾害防御水平和水安全保障能力。

基于气象要素驱动的流域水旱灾害智慧“四预”系统研究及应用探析

为助力数字孪生水利体系建设,以探索流域水文与气象的复杂联动关系为基础,研究长期气候变化和短期极端天气事件对流域水旱灾害发生频率的潜在影响,推动关键气象要素驱动数据集的多源数据融合和水文气象预测平台的构建,通过中短期极端气象事件的实时预报和同步解译,提升流域防洪抗旱决策制定及工作部署的能力和反应速度。研究成果对于推动数字孪生流域水旱灾害“四预”精细化和智慧化具有重要意义。

工业互联技术在校园供水系统中的应用探讨——以南京中医药大学二次供水泵站智慧化改造项目为例

高校作为社会用水大户,由于用水类型复杂、部分学校供水设施落后老化、水系统智慧化管理手段缺失或应用效果不达标等因素,导致校园供水系统存在运行安全性不高、水质水压无保证、管道漏损率偏高、管理效率低下等问题。南京中医药大学运用以工业互联技术为核心的一系列先进技术,改造建成智慧供水标准化泵站。通过智联供水设备改造,环境保障改造,蓄水池水质保障改造,压力调节系统改造等,并通过平台化管理提高运维管理效率,实现了良好的改造效益。

智慧灌溉大数据管理平台设计与应用

以粗放型为主的漫灌方式浪费水资源,探索高效节水农业灌溉意义重大。本研究研制了集数据采集、数据管理、数据可视化、分析决策和远程管理为一体的智慧灌溉大数据管理平台,旨在以数字化管理与智能化控制方式提升水资源利用效率。平台利用嵌入式、物联网、互联网、3S(GIS、GPS、RS)等技术,采取“1+1+N”的设计模式,构建作物需水预报模型与灌溉决策模型,基于B/S构架和Java语言,设计了1个灌溉数据中心、1个灌溉数据管理系统、4个应用系统,打造了智慧灌溉大数据管理平台。平台在河北、河南、山东、江苏等地区设有多个示范基地,汇聚了8个科研小组、24个试验基地的有关多种作物的生长、灌溉、气象、土壤等数据,平均采集数据18829条/天,帮助管理人员摸清家底;集成了多个团队的软件系统和62套物联网设备,及时、定量地呈现了农作物生长状况及环境状态,实现了农作区动态监测,助力生产决策;生成农作物需水预报和灌溉决策方案,完成了远程灌溉目标,并且经过实地试验,验证了自动灌溉的有效性,将灌溉水有效利用系数最高提升了31%以上;形成了大田产业、温室中心、数字科研和区域灌溉等不同专题可视化布局,满足多种农业场景应用需求。为农业生产和跨区域管理提供了便捷工具,为农业灌溉数字化系统搭建和应用提供了参考。

机器学习在水务行业中的应用现状与发展前景

机器学习是人工智能领域的主流算法,近年来水务行业的数据量呈指数级增加,以机器学习为主流算法的人工智能技术在水务行业中的应用逐步得到拓展。综述了机器学习在我国水务行业中的应用现状与发展前景。目前机器学习凭借其强大的数据挖掘能力在水资源优化调度、水处理厂运维、管网运维及城市雨洪管理方面具有较为广泛的应用,在水务信息化和数据资源深度挖掘与整合利用方面做出了突出贡献。未来机器学习能够助力双碳背景下污水厂提标改造,持续推动水务信息数字化向智能化转型。

离子匹配悬浮液驱提高碳酸盐岩油藏波及效率潜力与机理实验

水驱波及效率提升可以显著增强碳酸盐岩油藏开发效果。本文在考虑地层水离子组成基础上,借助体视显微镜和激光粒度仪配制了一种离子匹配悬浮液,其中悬浮的硫酸钙颗粒处于临界饱和状态,大部分颗粒粒径在10μm左右,静置后由于絮凝和聚集作用最大可到50μm。悬浮液的静态性质实验表明,由于硫酸钙“逆溶解度”特征和水合物的变化,其微粒会在小于45℃范围缓慢消失,并在90℃时迅速析出;矿化度和离子组成改变会使硫酸钙微粒浓度和粒径显著增大。在渗透率级差为4.7的岩心并联实验中,离子匹配悬浮液可使较低渗岩心的分流率最大提高约9.8%,最终采收率提高约4.3%。温度、压力和盐度的影响,以及注入水与地层水离子交换,使悬浮液中的饱和硫酸钙周期性溶解和沉淀,导致压力波动。压力波动使硫酸钙的溶解/沉淀特征复杂化,最终显著改变了水驱后期并联模型中的分流率。含有饱和硫酸钙的不稳定离子匹配悬浮液是从材料领域借鉴到石油领域的新尝试,具有一定提高波及效率作用。它比常规欠饱和离子匹配水驱过程中产生的不可控原位微粒更有效、更可靠、更环保。