How much would silica nanoparticles enhance the performance of low-salinity water flooding?

Nanofluids and low-salinity water(LSW)flooding are two novel techniques for enhanced oil recovery.Despite some efforts on investigating benefits of each method,the pros and cons of their combined application need to be evaluated.This work sheds light on performance of LSW augmented with nanoparticles through examining wettability alteration and the amount of incremental oil recovery during the displacement process.To this end,nanofluids were prepared by dispersing silica nanoparticles(0.1 wt%,0.25 wt%,0.5 wt% and 0.75 wt%)in 2,10,20 and 100 times diluted samples of Persian Gulf seawater.Contact angle measurements revealed a crucial role of temperature,where no wettability alteration occurred up to 80 ℃.Also,an optimum wettability state(with contact angle 22°)was detected with a 20 times diluted sample of seawater augmented with 0.25 wt% silica nanoparticles.Also,extreme dilution(herein 100 times)will be of no significance.Throughout micromodel flooding,it was found that in an oil-wet condition,a combination of silica nanoparticles dispersed in 20 times diluted brine had the highest displacement efficiency compared to silica nanofluids prepared with deionized water.Finally,by comparing oil recoveries in both water-and oil-wet micromodels,it was concluded that nanoparticles could enhance applicability of LSW via strengthening wettability alteration toward a favorable state and improving the sweep efficiency.

Performance improvement of ionic surfactant flooding in carbonate rock samples by use of nanoparticles

Various surfactants have been used in upstream petroleum processes like chemical flooding. Ultimately, the performance of these surfactants depends on their ability to reduce the interfacial tension between oil and water. The surfactant concentration in the aqueous solution decreases owing to the loss of the surfactant on the rock surface in the injection process. The main objective of this paper is to inhibit the surfactant loss by means of adding nanoparticles. Sodium dodecyl sulfate and silica nanoparticles were used as ionic surfactant and nanoparticles in our experiments, respectively. AEROSIL~？ 816 and AEROSIL~？200 are hydrophobic and hydrophilic nanoparticles. To determine the adsorption loss of the surfactant onto rock samples, a conductivity approach was used. Real carbonate rock samples were used as the solid phase in adsorption experiments. It should be noted that the rock samples were water wet. This paper describes how equilibrium adsorption was investigated by examining adsorption behavior in a system of carbonate sample（solid phase） and surfactant solution（aqueous phase）. The initial surfactant and nanoparticle concentrations were 500–5000 and 500–2000 ppm, respectively. The rate of surfactant losses was extremely dependent on the concentration of the surfactant in the system, and the adsorption of the surfactant decreased with an increase in the nanoparticle concentration. Also, the hydrophilic nanoparticles are more effective than the hydrophobic nanoparticles.

A Novel γ-Alumina Supported Fe-Mo Bimetallic Catalyst for Reverse Water Gas Shift Reaction

在反向的水气体，移动(RWGS ) 反应 CO2 被变换成能接着被用来生产象甲醇那样的有益的化学药品的公司。在现在的学习， Mo/Al2O3， Fe/Al2O3 和 Fe-Mo/Al2O3，催化剂是用受精方法的 synthesised。催化剂的结构用 X 光检查衍射(XRD ) 被学习， Brunauer-Emmett-Teller (赌注) 方法，诱导地联合的血浆原子排放分光计(ICP-AES ) ，温度规划了减小(H2-TPR ) ，公司化学吸着，精力散 X 光检查(EDX ) 和扫描电子显微镜学(SEM ) 技术。所有催化剂的运动性质为 RWGS 反应在一个批反应堆被调查。结果显示在 Fe-Mo/Al2O3 催化剂的结构的那瞬间存在作为与 Fe/Al2O3 相比提高它的活动。这改进可能由于更好的 Fe 分散和 Fe 种类的更小的粒子尺寸。Fe-Mo/Al2O3 催化剂的稳定性测试在一个固定的床反应堆和高公司收益被执行因为溪流上的时间的 60 h 被表明。3 阶段在新鲜、使用的催化剂的结构被发现的 Fe2 (MoO4 ) 。TPR 结果也显示 3 分阶段执行的 Fe2 (MoO4 ) 有低 reducibility，因此， 3 显著地分阶段执行的 Fe2 (MoO4 ) 在催化剂禁止留下的 Fe 氧化物的减小，导致了 Fe-Mo/Al2O3 的高稳定性催化剂。总的来说，这研究与高公司产量作为新奇催化剂介绍 Fe-Mo/Al2O3，几乎没有副产品并且为 RWGS 反应相当稳定。

问荆提取物页岩水化膨胀抑制剂性能及防膨机理

提出将问荆提取物作为页岩水化膨胀抑制剂,通过室内实验研究了其抑制页岩水化膨胀的性能,与氯化钾和聚胺进行了对比,并分析了其防膨机理。膨润土水化抑制实验、膨润土沉淀实验、动态线性膨胀实验、滚动回收实验和吸附量测定实验结果表明:问荆提取物可以抑制膨润土的水化,阻止黏土矿物在溶液中均匀分散,减小膨润土的膨胀,抑制钻屑在水中破碎和分散,且与氯化钾和聚胺性能相当。对问荆提取物的防膨机理进行分析后发现:其成分中具有活性烃基的物质与膨润土颗粒表面间氢键缔合,降低了膨润土颗粒表面的吸水率,从而抑制膨润土膨胀。除了具有良好的防膨能力,问荆提取物获取容易、价格低廉,且不会破坏生态环境,是一种理想的页岩水化膨胀抑制剂。

Experimental investigation into L-Arg and L-Cys eco-friendly surfactants in enhanced oil recovery by considering IFT reduction and wettability alteration

Surfactant flooding is an important technique used to improve oil recovery from mature oil reservoirs due to minimizing the interfacial tension(IFT)between oil and water and/or altering the rock wettability toward water-wet using various surfactant agents including cationic,anionic,non-ionic,and amphoteric varieties.In this study,two amino-acid based surfactants,named lauroyl arginine(L-Arg)and lauroyl cysteine(L-Cys),were synthesized and used to reduce the IFT of oil–water systems and alter the wettability of carbonate rocks,thus improving oil recovery from oil-wet carbonate reservoirs.The synthesized surfactants were characterized using Fourier transform infrared spectroscopy and nuclear magnetic resonance analyses,and the critical micelle concentration(CMC)of surfactant solutions was determined using conductivity,pH,and turbidity techniques.Experimental results showed that the CMCs of L-Arg and L-Cys solutions were 2000 and 4500 ppm,respectively.It was found that using L-Arg and L-Cys solutions at their CMCs,the IFT and contact angle were reduced from 34.5 to 18.0 and15.4 mN/m,and from 144°to 78°and 75°,respectively.Thus,the L-Arg and L-Cys solutions enabled approximately 11.9%and 8.9%additional recovery of OOIP(original oil in place).It was identified that both amino-acid surfactants can be used to improve oil recovery due to their desirable effects on the EOR mechanisms at their CMC ranges.

On the prediction of filtration volume of drilling fluids containing different types of nanoparticles by ELM and PSO-LSSVM based models

There is a direct link between the extent of formation damage and the filtration volume of the drilling fluids in hydrocarbon reservoirs.The filtration volume can be diminished by adding different additives to the drilling fluids.Recently,nanoparticles have been extensively used for enhancing the filtration characteristics of the drilling fluids.However,there is no reliable model for investigating the influence of this class of additives on the performance of drilling fluids.Hence in this study,two powerful tools ELM(extreme learning machine)and PSO-LSSVM(particle swarm optimization-least square support vector machine)are applied to determine the effect of various nanoparticles on the filtration volume.The assessment of the models is carried out by computing the statistical parameters,and it is found that ELM has a greater ability to predict the filtration volumes,while PSO-LSSVM performs satisfactorily too.The model predictions and experimental results are in excellent agreement as suggested by the values of root mean squared error(RMSE=0.2459),coefficient of determination(R^(2)=0.999),and mean relative error(MRE=2.028%)for the dataset.The statistical analysis shows that the suggested model can predict the filtration volume with great accuracy.Moreover,through sensitivity analysis of the input parameters,it is found that for a specified nanoparticle,the filtration volume is highly influenced by nanoparticle concentration and it is the essential variable for the optimization process.

Shale hydration inhibition characteristics and mechanism of a new amine-based additive in water-based drilling fluids

In this work,shale hydration Inhibition performance of tallow amine ethoxylate as a shale stabilizer in water based drilling fluid,was investigated through these tests:bentonite hydration inhibition test,bentonite sedimentation test,drill cutting recovery test,dynamic linear swelling test,wettability test,isothermal water adsorption test,and zeta potential test.The results showed that bentonite particles are not capable of being hydrated or dispersed in the mediums containing tallow amine ethoxylate;tallow amine ethoxylate had shown a comparable and competitive inhibition performance with potassium chloride as a common shale stabilizer in drilling industry.Some amine functional groups exist in tallow amine ethoxylate structure which are capable of forming hydrogen bonding with surfaces of bentonite particles.This phenomenon decreased the water adsorption on bentonite particles'surfaces which results in reduction of swelling.Tallow amine ethoxylate is also compatible with other common drilling fluid additives.

Novel Synthesis of Barbiturates

有不同的醛的 barbituric 酸的 Knovenagel 反应被用来综合新 barbiturates.This 是能习惯于 synthsis 的一个新奇方法与不同的巴比妥酸盐的新产生的各种各样的类型以前报导了。

Solving asphaltene precipitation issue in vertical wells via redesigning of production facilities

Precipitation of heavy hydrocarbon components such as Wax and Asphaltenes are one of the most challenging issues in oil production processes.The associated complications extend from the reservoir to refineries and petrochemical plants.Precipitation is most destructive when the affected areas are hard to reach,for example the wellbore of producing wells.This work demonstrates the effect of adjusting choke valve sizes on thermodynamic parameters of fluid flowing in a vertical well.Our simulation results revealed optimum choke valve sizes that could keep producing vertical wells away from Asphaltene precipitation.The results of this study were implemented on a well in Darquin Reservoir that had been experiencing asphaltene precipitation.Experimental analysis of reservoir fluid,Asphaltene tests and thermodynamic simulations of well column were carried out and the most appropriate size of choke valve was determined.After replacing the well's original choke valve with the suggested choke valve,the Asphaltene precipitation problem diminished.

Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool

Greater complexity is involved in the transient pressure analysis of horizontal oil wells in contrast to vertical wells,as the horizontal wells are considered entirely horizontal and parallel with the top and underneath boundaries of the oil reserve.Therefore,there is an essential need to estimate productivity of horizontal wells accurately to examine the effectiveness of a horizontal well in terms of technical and economic prospects.In this work,novel and rigorous methods based on two different types of intelligent approaches including the artificial neural network(ANN)linked to the particle swarm optimization(PSO)tool are developed to precisely forecast the productivity of horizontal wells under pseudo-steady-state conditions.It was found that there is very good match between the modeling output and the real data taken from the literature,so that a very low average absolute error percentage is attained(e.g.,<0.82%).The developed techniques can be also incorporated in the numerical reservoir simulation packages for the purpose of accuracy improvement as well as better parametric sensitivity analysis.

On the prediction of methane adsorption in shale using grey wolf optimizer support vector machine approach

With the advancement of technology,gas shales have become one of the most prominent energy sources all over the world.Therefore,estimating the amount of adsorbed gas in shale resources is necessary for the technical and economic foresight of the production operations.This paper presents a novel machine learning method called grey wolf optimizer support vector machine(GWO-SVM)to predict adsorbed gas.For this purpose,a data set containing temperature,pressure,total organic carbon(TOC),and humidity has been collected from several sources,and the GWO-SVM model was created based on it.The results show that this model has R-squared and root mean square error equal to 0.982 and 0.08,respectively.Also,the results ensure that the proposed model gives an excellent prediction of the amount of adsorbed gas compared to previously proposed models.Besides,according to the sensitivity analysis,among the input parameters,humidity has the highest effect on gas adsorption.

Technical and economic feasibility study of flue gas injection in an Iranian oil field

Nowadays,the non-hydrocarbon gases are the main sources for gas injection projects in different countries.The main advantages of the flue gas injection are low cost,readily available sources(which consists mainly of N2 and CO2)and low compressibility in comparison with other gases like CO2 or CH4(for a given volume at the same conditions).In addition,it occupies more space in the reservoir and it is an appropriate way for CO2 sequestering and consequently reducing greenhouse gases.In the aforementioned method,N2 and/or CO2 is injected into the oil reservoir for miscible and/or immiscible displacement of remaining oil.Moreover,a key parameter in the designing of a gas injection project is the minimum miscibility pressure(MMP)which is commonly calculated by running simulation case or implementing conventional correlations.From technical viewpoints,the lower MMP values are more flavor for miscible gas injection process due to lower injection pressure and consequently lower maintenance and lower injection costs.The main aim of this research is to investigate various gas injection methods(N2,CO2,produced reservoir gas,and flue gas)in one of the northern Persian gulf oil fields by a numerical simulation method.Moreover,for each scenario of gas injection technical and economical considerations are took into account.Finally,an economic analysis is implemented to compare the net present value(NPV)of the different gas injection scenarios in the aforementioned oil field.

Experimental assessment of hybrid smart carbonated water flooding for carbonate reservoirs

Different methods of enhanced oil recovery have been used to produce trapped oil.One of these methods is carbonated water injection in which CO2 contained water is injected in reservoirs in order to decrease free CO2 injection mobility,increase water viscosity and store/remove produced greenhouse CO2 gas safely.Another enhanced oil recovery method is smart water injection at which the ions in brine are modified in order to make controlled reactions with distributed ions on the surface of rock to cause more hydrocarbon recovery.Therefore,combination of these two methods may also have a great effect on enhancing oil recovery or may result in recovery factor less than each method used alone.In this paper hybrid smart carbonated water injection method is investigated to study its applicability in oil recovery using core flooding setup.The experimental core flooding setup was designed to perform different types of EOR methods for the sake of recovery comparison with the new hybrid method.The effect of both brine content and volume of CO2 is determining in hybrid EOR assessment.The main findings of this work show that the hybrid smart carbonated water results in the highest recovery factor in comparison to the most well-known EOR methods for carbonate cores.

Toward connectionist model for predicting bubble point pressure of crude oils: Application of artificial intelligence

Knowledge about reservoir fluid properties such as bubble point pressure(Pb)plays a vital role in improving reliability of oil reservoir simulation.In this work,hybrid of swarm intelligence and artificial neural network(ANN)as a robust and effective method was executed to determine the Pb of crude oil samples.In addition,the exactly precise Pb data samples reported in the literatures were employed to create and validate the PSO-ANN model.To prove and depict the reliability of the smart model developed in this study for estimating Pb of crude oils,the conventional approaches were applied on the same data set.Based on the results generated by PSO-ANN model and other conventional methods and equation of states(EOS),the PSO-ANN model is a reliable and accurate approach for estimating Pb of crude oils.This is certified by high value of correlation coefficient(R2)and insignificant value of average absolute relative deviation(AARD%)which are obtained from PSO-ANN outputs.Outcomes of this study could help reservoir engineers to have better understanding of reservoir fluid behavior in absence of reliable and experimental data samples.

Kinetic modeling of cement slurry synthesized with Henna extract in oil well acidizing treatments

Acidizing treatment in petroleum reservoirs is a short-term and viable strategy to preserve the productivity of a well.There is a major concern for the degradation of cement sheath integrity,leading to poor zonal isolation and environmental issues.Therefore,it is essential to understand how the cement behaves when attacked by hydrochloric acid.In this study,a cement slurry by incorporation of the Henna extract,as an environmentally friendly cement additive,was synthesized as a potential solution to solve this problem.The characteristics of the treated cement slurry were compared with a reference slurry(w/c?0.44)which is composed of only cement and water.A kinetic study was carried out to evaluate the adsorption behavior of the cement slurries exposed to an acid solution with 0.1 M HCl in a range of 25 to 55C conditions.The features of the cement slurries were evaluated by multiple analytical techniques such as XRD,FTIR,TG,and DSC analysis.From the experimental data,it is concluded that the second-order Lagergren kinetic model revealed to be the best in describing kinetic isotherms taken,because the margin between experimental and calculated values was minor for this model.The results of the characterization and HCl interaction kinetic studies underlined the prominent protective role of Henna extract-modified cement slurry in the enhancement of the cement resistance against acid attack and utilization in environmentally favorable oil well acidizing treatments.

Robust correlation to predict dew point pressure of gas condensate reservoirs

When the bottom-hole flowing pressure in a gas condensate reservoir drops below the dew point pressure,liquid starts to build up around the well bore resulting in gas productivity decline.For this reason it is important to be able to accurately either measure or estimate the dew point pressure.The condensate formed in the reservoir will not flow until its saturation reaches the critical saturation and in many cases it might not be entirely recovered.It order to maximize gas production and condensate recovery,the reservoir pressure must be maintained close to the dew point pressure.Several attempts have been made to predict the dew point pressure in case the gas sample becomes unavailable or measured value is unreliable.Unfortunately,most of these attempts have minor success rates and are based on limited data.In this paper we present a robust,cheap,and easy model for predicting the dew point pressure for gas condensate reservoirs.The new model is an intelligent based model called“Gene Expression Programming”that is carried out to generate a precise and accurate correlation to estimate the dew point pressure in condensate gas reservoirs.The new model has been trained and tested using a large data bank collected for the literature.Precision of the suggested correlation has been compared to published correlations.The validity of this model has also been compared to experimental data and other published correlations.

Evolving simple-to-use method to determine watereoil relative permeability in petroleum reservoirs

In the current research,a new approach constructed based on artificial intelligence concept is introduced to determine water/oil relative permeability at various conditions.To attain an effective tool,various artificial intelligence approaches such as artificial neural network(ANN),hybrid of genetic algorithm and particle swarm optimization(HGAPSO)are examined.Intrinsic potential of feed-forward artificial neural network(ANN)optimized by different optimization algorithms are composed to estimate water/oil relative permeability.The optimization methods such as genetic algorithm,particle swarm optimization and hybrid approach of them are implemented to obtain optimal connection weights involved in the developed smart technique.The constructed intelligent models are evaluated by utilizing extensive experimental data reported in open literature.Results obtained from the proposed intelligent tools were compared with the corresponding experimental relative permeability data.The average absolute deviation between the model predictions and the relevant experimental data was found to be less than 0.1%for hybrid genetic algorithm and particle swarm optimization technique.It is expected that implication of HGAPSO-ANN in relative permeability of water/oil estimation leads to more reliable water/oil relative permeability predictions,resulting in design of more comprehensive simulation and further plans for reservoir production and management.

Investigation of quality factor frequency content in vertical seismic profile for gas reservoirs

Since long ago, indirect study of the underground layers properties has been interesting to geologists.One method for this study was seismography which gained great interest besides other tools due to thedifferent identity of waves and energy attraction phenomena in different layers. Vertical seismic profiling(VSP) is considered as a valuable method in oil and gas exploration. This method is used to estimate therock properties in a well. In seismic operations elastic waves are sent down to the underground. Part ofthe waves’ energy is reflected after passing through the earth layers and are received by receivers on theground level. The received data determine the situation of the underneath layers after being processed,and one of the most important applications of seismic data is in the oil and gas exploration field. Qualityfactor is one of the most important seismic detectors that shows itself apparently in VSP data results. Themost substantial purpose of this study is to investigate the frequency content of the quality factor.

A predictive model of chemical flooding for enhanced oil recovery purposes:Application of least square support vector machine

Applying chemical flooding in petroleum reservoirs turns into interesting subject of the recent researches.Developing strategies of the aforementioned method are more robust and precise when they consider both economical point of views(net present value(NPV))and technical point of views(recovery factor(RF)).In the present study huge attempts are made to propose predictive model for specifying efficiency of chemical flooding in oil reservoirs.To gain this goal,the new type of support vector machine method which evolved by Suykens and Vandewalle was employed.Also,high precise chemical flooding data banks reported in previous works were employed to test and validate the proposed vector machine model.According to the mean square error(MSE),correlation coefficient and average absolute relative deviation,the suggested LSSVM model has acceptable reliability;integrity and robustness.Thus,the proposed intelligent based model can be considered as an alternative model to monitor the efficiency of chemical flooding in oil reservoir when the required experimental data are not available or accessible.

Prediction of reservoir brine properties using radial basis function (RBF) neural network

Aquifers,which play a prominent role as an effective tool to recover hydrocarbon from reservoirs,assist the production of hydrocarbon in various ways.In so-called water flooding methods,the pressure of the reservoir is intensified by the injection of water into the formation,increasing the capacity of the reservoir to allow for more hydrocarbon extraction.Some studies have indicated that oil recovery can be increased by modifying the salinity of the injected brine in water flooding methods.Furthermore,various characteristics of brines are required for different calculations used within the petroleum industry.Consequently,it is of great significance to acquire the exact information about PVT properties of brine extracted from reservoirs.The properties of brine that are of great importance are density,enthalpy,and vapor pressure.In this study,radial basis function neural networks assisted with genetic algorithm were utilized to predict the mentioned properties.The root mean squared error of 0.270810,0.455726,and 1.264687 were obtained for reservoir brine density,enthalpy,and vapor pressure,respectively.The predicted values obtained by the proposed models were in great agreement with experimental values.In addition,a comparison between the proposed model in this study and a previously proposed model revealed the superiority of the proposed GA-RBF model.