An overview of chemical enhanced oil recovery and its status in India

India is currently producing crude oil from matured fields because of insufficient discoveries of new fields.Therefore,in order to control the energy crisis in India,enhanced oil recovery(EOR)techniques are required to reduce the import of crude from the OPEC(Organization of the Petroleum Exporting Countries).This review mentions chemical EOR techniques(polymers,surfactants,alkali,nanoparticles,and combined alkali-surfactant-polymer flooding)and operations in India.Chemical EOR methods are one of the most efficient methods for oil displacement.The efficiency is enhanced by interfacial tension(IFT)reduction using surfactants and alkali,and mobility control of injected water is done by adding a polymer to increase the volumetric sweep efficiency.This paper also reviews the current trend of chemical EOR,prospects of chemical EOR in Indian oilfields,the development of chemical EOR in India with their challenges raising with economics,and screening criteria for chemical EOR implementation on the field scale.Furthermore,the review gives a brief idea about chemical EOR implementation in Indian oilfields in future prospects to increase the additional oil recovery from existing depleted fields to reduce the import of crude oil.The outcome of this review depicts all chemical EOR operations and recovery rates both at the laboratory scale and field scale around the country.The additional recovery rates are compared from various chemical EOR methods like conventional chemical flooding methods and conventional chemicals combined with nanoparticles on a laboratory scale.The development of chemical EOR in the past few decades and the EOR policy given by the government of India has been mentioned in this review.The analysis provides an idea about enhanced recovery screening and implementation of chemical EOR methods in existing fields will significantly reduce the energy crisis in India.

Comparative assessment of mechanical and chemical fluid diversion techniques during hydraulic fracturing in horizontal wells

The application of fluid diversion during hydraulic fracturing is an evolving technology and has become popular amongst E&P operators over the past few years.The primary objective of the fluid diversion is to improve hydraulic fracturing treatment by increasing stimulated reservoir volume and improving hydrocarbon recovery.This is possible by achieving any of the following objectives:creating uniform distribution of treatment slurry within the target zone;treating unstimulated and under-stimulated zones;or by increasing fracture density by creating a complex fracture network.The fluid diversion application is also helpful in decreasing the number of stages(by increasing stage length)for multi-stage plug-n-perf(PnP)fracturing treatment.It is also applied to prevent fracture-driven interactions between adjacent wells,which is currently a major issue,especially in shale.In addition,for successful refracturing treatment,the diverter application is essential for isolating the existing fractures and redirecting the treatment slurry to the desired unstimulated zones.The diversion methods can be broadly categorized into the mechanical and chemical diversion.Several established mechanical diversion techniques are frac plugs,expandable casing patches,expandable liners,swellable packers,straddle packer assembly,sand plugs,frac sleeves,perforation ball sealers,and limited entry technique.The different chemical diversion techniques are particulates,fibers,gels,surfactants,perforation pods,and composite diverting.This paper describes the current status of established mechanical and chemical diverter technologies and examines their comparative advantages and challenges.Various techniques are suitable for diverter application,but the technique is selected based on the desired objective and conditions of the wellbore and reservoir.The general guidelines for selecting diversion techniques and operational considerations are also provided in the paper.The diagnosis of diversion treatment plays an essential role in diversion technique selection and optimization of selection parameters for the subsequent treatments.Therefore,the application of conventional surface pressure monitoring techniques and advanced diagnostic tools to evaluate diversion effectiveness are briefly described.Presently no standard laboratory testing method is established for the performance evaluation of diverting agents.Therefore,researchers have implemented various laboratory methods,which are briefly summarized in the paper.Significant insight into the diversion technology and guidelines for its selection and successful implementation is provided to help engineers to increase the effectiveness of hydraulic fracturing treatments.The limitations of individual diversion techniques are clarified,which provide the future scope of research for improvement in various diversion technologies.

Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of energy.Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions.But the integration of distributed energy sources and increase in electric demand enhance instability in the grid.Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid.Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost reduction.But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data.Machine learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load data.The purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among data.Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead.The study is very helpful in grid stability and peak load control management.

Investigating the impact of atmospheric parameters on sea ice variability in and around Svalbard

Recent research has shown that winter warmings are phenomenally high compared to summer warmings over the poles,especially over the Arctic.Taking the current scenario into account,this paper attempts to understand the atmospheric variables causing sea ice variability over and around the region of Svalbard for seasons;winter,spring,summer and autumn for the span of 42 years(1979-2021).The variability in atmospheric and oceanic parameters namely temperature,precipitation,wind speed,and sea surface salinity are analysed over inter-spatial,inter-seasonal and inter-annual domains.Winters are characterized by inter-annual increasing trend in temperature.During 1981-1990 the rise from the decadal mean is found to be 0.39 K·a^(-1),during 1991-2000 it is 0.20 K·a^(-1),during 2001-2010 it is 0.04 K·a^(-1) and during 2011-2020 it is 0.23 K·a^(-1).Interestingly while considering inter-spatial domains,the region southwest to Svalbard seems to be wetter(0.05 mm·(10 a)-1)compared to its northeast(-0.03 mm·(10 a)-1).Across all the three domains,wind speeds are highest during autumn and then decrease subsequently through summer,spring and are least during winter.Wind is predominantly from the south,and hence it is suspected to carry hot Atlantic air.Additionally,the significant role of salinity in the ocean also plays a key role in governing the fate of sea ice conditions.The long-term forecasts of temperature over seaice of Svalbard are alarming especially for the winter ice(r=-0.84).Correlation matrices between atmospheric and sea ice parameters are shown to gain a better understanding on their inter relation.

A comprehensive analysis of the role of molecular docking in the development of anticancer agents against the cell cycle CDK enzyme

Cancer is considered one of the most lethal diseases responsible for causing deaths worldwide.Although there have been many breakthroughs in anticancer development,cancer remains the major cause of death globally.In this regard,targeting cancer-causing enzymes is one of the efficient therapeutic strategies.Biological functions like cell cycle,transcription,metabolism,apoptosis,and other depend primarily on cyclin-dependent kinases(CDKs).These enzymes help in the replication of DNA in the normal cell cycle process,and deregulation in the functioning of any CDK can cause abnormal cell growth,which leads to cancer.This review is focused on anticancer drug discovery against cell cycle CDK enzyme using an in silico technique,i.e.,molecular docking studies.Molecular docking helps in deciphering the key interactions formed within the inhibitor and the respective enzyme.This concise study provides an overview of the most current in silico research advancements made in the field of anticancer drug discovery.The findings presented in the current review article can help in understanding the nature of inhibitor-target interactions and provide information on the structural and molecular prerequisites for the inhibition of cell cycle CDKs.

EBAKE-SE: A novel ECC-based authenticated key exchange between industrial IoT devices using secure element

Industrial IoT(IIoT)aims to enhance services provided by various industries,such as manufacturing and product processing.IIoT suffers from various challenges,and security is one of the key challenge among those challenges.Authentication and access control are two notable challenges for any IIoT based industrial deployment.Any IoT based Industry 4.0 enterprise designs networks between hundreds of tiny devices such as sensors,actuators,fog devices and gateways.Thus,articulating a secure authentication protocol between sensing devices or a sensing device and user devices is an essential step in IoT security.In this paper,first,we present cryptanalysis for the certificate-based scheme proposed for a similar environment by Das et al.and prove that their scheme is vulnerable to various traditional attacks such as device anonymity,MITM,and DoS.We then put forward an interdevice authentication scheme using an ECC(Elliptic Curve Cryptography)that is highly secure and lightweight compared to other existing schemes for a similar environment.Furthermore,we set forth a formal security analysis using the random oracle-based ROR model and informal security analysis over the Doleve-Yao channel.In this paper,we present comparison of the proposed scheme with existing schemes based on communication cost,computation cost and security index to prove that the proposed EBAKE-SE is highly efficient,reliable,and trustworthy compared to other existing schemes for an inter-device authentication.At long last,we present an implementation for the proposed EBAKE-SE using MQTT protocol.

Geochemical analysis and quality assessment of geothermal water in Gujarat,India

Geothermal water can have a wide range of direct applications,including drinking,domestic use,irrigation and industrial use.This study analyses geothermal water from Dholera,Diu,Gandhar,Kutch,Porbandar,Tulsishyam,and Unai regions located in Gujarat,India,to determine the hydrochemistry for establishing the water's suitability for drinking and irrigation purposes.From each region,three different water samples were collected.For drinking purpose,total eleven water quality parameters such as pH,electrical conductivity,total dissolved solids,calcium,magnesium,sodium,potassium,chloride,sulphate,carbonate and bicarbonate were evaluated.Piper and Durov plots were used to understand the ionic composition and evolution of water.Whereas for irrigation,the parameters such as total dissolved solids,sodium absorption ration,electrical conductivity,and potential salinity were used for water quality check.Wilcox Plot was plotted to understand the sodium and salinity hazard of water.Water quality index and irrigation water quality index were used to assess the water quality for making the analytical procedure easy.The findings indicate that the water samples from the Dholera and Diu areas had extremely high values for both the water quality index and total dissolved solids content.Water in these places may be appropriate for making salt.All the other samples show adequacy for utilization in irrigation purpose.

Carbon mitigation using CarbFix,CO_(2)plume and carbon trading technologies

Following China and the United States,India is the third highest CO_(2)emitter in the world.With its fast rising population and an industry primarily reliant on coal and oil,the country's emissions will continue to rise unless appropriate measures are taken.In India generally the carbon sequestration is done by storing CO_(2)in sedimentary basins where it gets capped by the impermeable cap rocks.The study aims to unfold the other techniques of carbon sequestration in subsurface formations,except for narrating the different types of carbon capture and sequestration techniques along with its storage in different types of environments.For instance,abiotic and biotic environments for CO_(2)sequestration.It further talks about carbon injections of oceanic,geological,and terrestrial types,to name just a few.Types of carbon emission sources are discussed too.In this paper three different approaches are described for carbon mitigation,namely CarbFix,CO_(2)Plume technology and Carbon Trading.A prefeasibility study was done in order to implement the three techniques in Dholera geothermal region in Gujarat,India.In literature review,it can be seen how the three techniques can help in permanent mitigation of captured carbon through carbonate mineralization,power generation and industrial applications.For any project government and public participation is important.In this regard the study gives a brief understanding about the steps to be taken by government and the public to promote such projects for carbon mitigation.Besides,the steps to be taken in such projects for health and risk assessment were talked about in literature review too.

A comprehensive review of machine learning techniques on diabetes detection

Diabetes mellitus has been an increasing concern owing to its high morbidity,and the average age of individual affected by of individual affected by this disease has now decreased to mid-twenties.Given the high prevalence,it is necessary to address with this problem effectively.Many researchers and doctors have now developed detection techniques based on artificial intelligence to better approach problems that are missed due to human errors.Data mining techniques with algorithms such as-density-based spatial clustering of applications with noise and ordering points to identify the cluster structure,the use of machine vision systems to learn data on facial images,gain better features for model training,and diagnosis via presentation of iridocyclitis for detection of the disease through iris patterns have been deployed by various practitioners.Machine learning classifiers such as support vector machines,logistic regression,and decision trees,have been comparative discussed various authors.Deep learning models such as artificial neural networks and recurrent neural networks have been considered,with primary focus on long short-term memory and convolutional neural network architectures in comparison with other machine learning models.Various parameters such as the root-mean-square error,mean absolute errors,area under curves,and graphs with varying criteria are commonly used.In this study,challenges pertaining to data inadequacy and model deployment are discussed.The future scope of such methods has also been discussed,and new methods are expected to enhance the performance of existing models,allowing them to attain greater insight into the conditions on which the prevalence of the disease depends.

Is machine learning redefining the perovskite solar cells?

Development of novel materials with desirable properties remains at the forefront of modern scientific research.Machine learning(ML),a branch of artificial intelligence,has recently emerged as a powerful technology in optoelectronic devices for the prediction of various properties and rational design of materials.Metal halide perovskites(MHPs)have been at the centre of attraction owing to their outstanding photophysical properties and rapid development in solar cell application.Therefore,the application of ML in the field of MHPs is also getting much attention to optimize the fabrication process and reduce the cost of processing.Here,we comprehensively reviewed different applications of ML in the designing of both MHP absorber layers as well as complete perovskite solar cells(PSCs).At the end,the challenges of ML along with the possible future direction of research are discussed.We believe that this review becomes an indispensable roadmap for optimizing materials composition and predicting design strategies in the field of perovskite technology in the future.

Integrating machine learning and blockchain to develop a system to veto the forgeries and provide efficient results in education sector

Although the education sector is improving more quickly than ever with the help of advancing technologies,there are still many areas yet to be discovered,and there will always be room for further enhancements.Two of the most disruptive technologies,machine learning(ML)and blockchain,have helped replace conventional approaches used in the education sector with highly technical and effective methods.In this study,a system is proposed that combines these two radiant technologies and helps resolve problems such as forgeries of educational records and fake degrees.The idea here is that if these technologies can be merged and a system can be developed that uses blockchain to store student data and ML to accurately predict the future job roles for students after graduation,the problems of further counterfeiting and insecurity in the student achievements can be avoided.Further,ML models will be used to train and predict valid data.This system will provide the university with an official decentralized database of student records who have graduated from there.In addition,this system provides employers with a platform where the educational records of the employees can be verified.Students can share their educational information in their e-portfolios on platforms such as LinkedIn,which is a platform for managing professional profiles.This allows students,companies,and other industries to find approval for student data more easily.

Crime forecasting:a machine learning and computer vision approach to crime prediction and prevention

A crime is a deliberate act that can cause physical or psychological harm,as well as property damage or loss,and can lead to punishment by a state or other authority according to the severity of the crime.The number and forms of criminal activities are increasing at an alarming rate,forcing agencies to develop efficient methods to take preventive measures.In the current scenario of rapidly increasing crime,traditional crime-solving techniques are unable to deliver results,being slow paced and less efficient.Thus,if we can come up with ways to predict crime,in detail,before it occurs,or come up with a“machine”that can assist police officers,it would lift the burden of police and help in preventing crimes.To achieve this,we suggest including machine learning(ML)and computer vision algorithms and techniques.In this paper,we describe the results of certain cases where such approaches were used,and which motivated us to pursue further research in this field.The main reason for the change in crime detection and prevention lies in the before and after statistical observations of the authorities using such techniques.The sole purpose of this study is to determine how a combination of ML and computer vision can be used by law agencies or authorities to detect,prevent,and solve crimes at a much more accurate and faster rate.In summary,ML and computer vision techniques can bring about an evolution in law agencies.

医学云:一项关于医疗数据数字化整合的研究

对于身心健康的重要性,新型冠状病毒肺炎(COVID-19)的大流行给我们上了宝贵的一课。即使是科技高度发展的今天,仍未开发出一个系统,能完全使个人医疗数据数字化,并提供医患双方随时访问这些信息的权限。此外,政府机关也无法确认某个医疗单位的合法性。本研究融合现代科技及传统路径,突出人工智能(AI)与传统中医(TCM)相结合的场景,提出了可发展传统路径的方法。本研究的主要目的是向政府机关、医生、护士及患者提供可轻松访问相关数据的一站式平台。通过该平台还可对医生的资质进行查验。数据是每个机构最重要的资产之一,数据泄露可能会将用户的生命置于风险之中。因此,数据安全至关重要,必须优先考虑。我们所提出的研究方法是基于云计算技术,可确保数据的安全性,避免任何形式的数据泄露。本研究还考虑到了在云端建立类似的基础架构时可能遇到的困难,并克服了在项目期间所面临的各种障碍,为将来创新的可能预留了足够空间。综上所述,本研究围绕医疗数据数字化展开,提出了实现医疗数据数字化的一些可能途径。并且,还针对相关问题如数据安全和数字化的潜在难题进行了探讨。

Accelerating the renewable energy sector through Industry 4.0:Optimization opportunities in the digital revolution

The fourth industrial revolution introduced the concept of Industry 4.0,which refers to the digitalization of the modern world and the inclusion of digital industry in major industrial sectors.Within this context,we investigated how Industry 4.0 technologies could transform conventional factories into smart factories and contribute to the development of a circular economy with significant economic,environmental,and social advantages.The primary goal of this study was to assess the potential of Industry 4.0 technologies in optimizing energy production from renewable resources.Specifically,we examined how various Industry 4.0 technologies,including Blockchain,the Internet of Things(loT),and Cloud Computing,contributed to energy generation,transmission,and trade.In addition,we investigated solutions to issues such as intermittent power supply,low efficiency,grid bottleneck,wastage of excess power,and unpredictable energy markets.Furthermore,we discussed two case studies based on circular economies,sustainable development goals,and Industry 4.o-based data analysis platforms.The study concludes by discussing the challenges faced by industries in adapting to the digital revolution and the potential uses of modern-day digital technologies to advance the energy sector and society as a whole.

Optimization of gas lift system for well performance improvement in Asmari formation:A techno-economic perspective

Well productivity in the Asmari carbonate formation of southwest Iran has decreased in recent years as a result of production issues.The production rate must be maintained below 1500 STB/day to prevent water coning.In this study,a gas lift well is modeled using data from one of the producing wells of this field.Nodal analysis is performed using lift-gas injection rates and wellhead pressures at different reservoir pressures and water cut conditions to optimize production.Economic aspects are considered to optimize the artificial gas injection rates at different tubing head pressures and water cut conditions.Increasing the lift-gas injection rate from 0.4 MMscf/day to 1 MMscf/day enhances the oil production rate by 37.71%and 43.89%for 10%and 30%water cut conditions,respectively.Gas injection rates of 5.2 MMscf/day and 5.4 MMscf/day are determined to be economically optimal for 30%water cut with tubing head pressures of 260 psig and 270 psig,respectively.

Diverting agents in the oil and gas industry:A comprehensive analysis of their origins,types,and applications

During the life of a well,treatments are carried out to boost productivity by stimulating initially unproduced zones.These treatments include hydraulic fracturing,matrix acidization,and acid fracturing,among others.Hydraulic fracturing treatment is generally applied to deeper reservoirs of oil or natural gas for enhanced recovery.By infusing proppant,water,and chemicals under extreme pressure during the fracturing procedure,fissures in and beneath the reservoir layer can be accessed and expanded.Another stimulating procedure,matrix acidization,involves injecting acid down the drilling hole to permeate the rock fissures at stresses lower than the fracture stress.In addition,carbonate reservoir acid fracturing stimulation is commonly used as an acid treatment technique whereby a pressure greater than the formation disintegration pressure or spontaneous fracture closure pressure is used to compress acid into the reservoir.These treatments allow existing wells to sustain hydrocarbon production without new wells being drilled.Diverters,when employed efficiently,can prevent the need to use a rig to provide momentary physical barriers,thus lowering the cost of the workover.Recent improvements in diversion technology make use of a variety of degradable particles that act as momentary bridges,either at the perforation entries or inside the existing fractures.The aim of this study is to introduce different types of mechanical and chemical diverters used to enhance the productivity of wells.This study explains the concepts of different types of diverters and their applications in several formations,it will also help readers to understand the selection procedures based on the suitability and requirements of diverter use by case studies from around the world.

Explainable Artificial Intelligence (XAI) techniques for energy and powersystems: Review, challenges and opportunities

Despite widespread adoption and outstanding performance, machine learning models are considered as ‘‘blackboxes’’, since it is very difficult to understand how such models operate in practice. Therefore, in the powersystems field, which requires a high level of accountability, it is hard for experts to trust and justify decisionsand recommendations made by these models. Meanwhile, in the last couple of years, Explainable ArtificialIntelligence (XAI) techniques have been developed to improve the explainability of machine learning models,such that their output can be better understood. In this light, it is the purpose of this paper to highlight thepotential of using XAI for power system applications. We first present the common challenges of using XAI insuch applications and then review and analyze the recent works on this topic, and the on-going trends in theresearch community. We hope that this paper will trigger fruitful discussions and encourage further researchon this important emerging topic.

Comparative studies on numerical sensitivity of different scenarios of enhanced oil recovery by water-alternating-gas(CO_(2))injection

Enhanced oil recovery by CO_(2) injection technology(CO_(2)-EOR)plays a crucial role in enhancing oil production and the permanent sequestration of anthropogenic CO_(2) in depleted oil reservoirs.However,the availability of CO_(2) in oil field locations and its mobility in contrast with reservoir fluids are prime challenges in CO_(2)-EOR.The cost of CO_(2) and its availability at the oil fields has prompted investigations on efficient injection of CO_(2) at the fields to achieve the best sweep efficiency possible.Injection strategies such as water-alternating-gas(WAG),simultaneous vertical and horizontal WAG,simultaneous water injection into the aquifer and vertical WAG,water and gas injection simultaneously but separately(SS-WAG),and water and gas injection simultaneously but not separately(SNS-WAG)play a significant role,as well as the purity of CO_(2).In this work,the significance of the above criteria was investigated indi-vidually and in combination.The coupled sequence of injection rate,soaking time,WAG ratio,and purity of injected CO_(2) for enhancement of oil production were delineated.A realistic reservoir simulation model conceptualizing the CO_(2)-EOR system with five spot injection patterns was developed by the company CMG.The history-matched model that was developed was used to investigate the sensitivity of the coupled effects to the criteria listed above on oil recovery.Numerical investigations quantitatively emphasized that purity and soaking time of CO_(2) have an inverse effect in the oil production rate and that SNS-WAG resulted in a better oil production rate than SS-WAG.

Single-source Switched-capacitor Boost Nine-level Inverter with Reduced Components

Increasing demands for improvement in power quality and power capacity have contributed to development of switched-capacitor multilevel inverters(SCMLIs).Recently developed SCMLIs enable single-stage voltage boosting,as well as inversion resulting in step-up ac output.Towards reduction in number of components,this paper introduces a boost type singlesource nine-level(9-level)SCMLI employing two capacitors and three diodes.Owing to the series-parallel connection process,capacitor voltages are inherently balanced and assist in quadruple voltage boosting from a single-source.Maximum voltage stress across semiconductor devices is limited to twice input voltage only.Using a minimum number of components,the proposed SCMLI can be extended to increase voltage levels without additional dc input.Each extension module adds two additional voltage steps in the output while maintaining maximum voltage stress the same as 9-level circuit.Followed by in-depth analysis of circuit operation and power losses,a thorough comparison of recently developed single-phase 9-level MLIs is carried out,which verifies design superiority.Extensive simulation and experimental results are presented to verify the prominent features of the 9-level SCMLI under dynamic operating conditions.

Effects of nanoparticles, polymer and accelerator concentrations, and salinity on gelation behavior of polymer gel systems for water shut-off jobs in oil reservoirs

Excess water production is one of the crucial complications in the oil industry,leading to a rapid decline in oil production.To overcome this problem,different polymer gels are used to block the water's path to reduce water production.In the present work,polymer gel systems were prepared with polymers namely partially hydrolyzed polyacrylamide(PHPA),organic crosslinkers like hexamine,and hydroquinone with the incorporation of silica and alumina nanoparticles.Nanoparticles are used to enhance the stability of the gel framework in high salinity and high temperature reservoir environment.When designing the polymer gel system,factors such as pH,thermal stability,brine composition,injection rate,and chemical concentration were considered.Concentrations of PHPA and nanoparticles were varied from 1 to 2 wt% and 0.25e1.0 wt% respectively for the preparation of different gel systems.The concentration of the organic crosslinker was extended from 5000 to 11000 ppm for investigating the effect on gelation time.Brine concentration was chosen from 2 to 8 wt% to find the impact of high salinity.Succinic acid as an accelerator was also used to study the effect on gelation time,and it was found that the gelation time is reduced as the concentration of succinic acid increases.The prepared polymer solution was taken in a test tube and was kept inside a hot air oven at 95℃ to perceive the gelation time and nature of the produced gel.Results showed that nanoparticles do not influence the gelation time,but they considerably affect gel stability.However,concentrations of polymer,accelerator,and salt(salinity)have significant effects on the gelation behavior of the gel systems.