Applying Fuzzy c-means approach as a novel method for prediction of interfacial tension between carbon dioxide and hydrocarbons

The worldwide demand for energy increases and also the price of crude oil increases so these reasons have been caused the searchers have motivated to investigate enhanced of oil recovery (EOR) processes. The carbon dioxide injection is recognized as one of the favorable approaches of EOR because of high displacement efficiency, environmentally aspects and lower cost. The Interfacial tension between crude oil and carbon dioxide is known as one of the critical factors which affect the performance of injection. The main objective of the present investigation is development of Fuzzy c-means (FCM) approach as novel method to estimate interfacial tension between carbon dioxide and hydrocarbons as function of pressure, temperature, liquid and gas densities and molecular weight of alkane. The performance of predicting model was evaluated statistically and graphically and the results confirmed the ability of the model to predict interfacial tension between carbon dioxide and hydrocarbons.     

VISCOSITY PREDICTION OF CARBON DIOXIDE HYDROCARBON MIXTURES USING THE FRICTION THEORY

The general one-parameter f-theory model has been used in conjunction with the SRK and the PR EOS to predict the viscosity of well-defined carbon dioxide + hydrocarbon mixtures. The predicted viscosities are within the uncertainty appropriate for most industrial applications. Although the studied mixtures are simple representations of real oil mixtures with carbon dioxide, the f-theory approach can easily be extended to more complex scenarios, such as the simulation of carbon dioxide enhance oil recovery. Additionally, a comparison with the LBC model, which is a widely used model in the oil industry, has been carried out. In contrast to the f-theory models, the strong dependency that the LBC model has on the accuracy of the density is clearly evident for the kind of mixtures studied in this work. Furthermore, it is shown how the phase behavior complexity that carbon dioxide + hydrocarbon mixtures develop may have a direct influence on the performance of the viscosity modeling and prediction.     

Applying LSSVM algorithm as a novel and accurate method for estimation of interfacial tension of brine and hydrocarbons

One of the critical parameters in petroleum and chemical engineering is the interfacial tension between brine and hydrocarbon which has major effects on trapping and residual oil in reservoir pore throat so it becomes one of the interesting topics in enhancement of oil recovery in this work Least squares support vector machine (LSSVM) algorithm was applied as a novel predicting machine for prediction of interfacial tension of brine and hydrocarbons in terms of hydrocarbon carbon number, temperature, pressure and ionic strength of brine. A total number of 175 interfacial tensions were collected from literature in the purpose of training and testing of the model. The root mean squared error (RMSE), average absolute relative deviation (AARD) and the coefficient of determination (R²) were calculated overall datasets as 0.23964, 0.27444 and 0.98509 respectively. The results of study showed that predicting LSSVM machine can be applicable for estimation of interfacial tension and EOR processes.     

Application of Grid partitioning based Fuzzy inference system method as a novel approach for prediction of interfacial tension of hydrocarbon and carbon dioxide

Nowadays the importance of enhanced oil recovery (EOR) processes increases because of increasing demand of energy and declination of oil reservoirs. Due to this fact the researchers attracted to study performance of EOR methods. one of the high efficient methods is carbon dioxide injection which is favorable because of low cost and environmental friendly viewpoints. One of important parameters which have straight effect on recovery of injection is interfacial tension between carbon dioxide and hydrocarbons. In the present investigation the main objective is proposing the Grid partitioning based Fuzzy inference system method as novel approach to predict interfacial tension of carbon dioxide and hydrocarbon in terms of temperature, pressure, liquid and gas densities and molecular weight of alkane. The coefficients of determination for different datasets of training and testing of estimating algorithm are determined as 0.9919 and 0.9899. This results express the algorithm has potential of estimating interfacial tension of hydrocarbons and carbon dioxide.     

Utilization of Grid partitioning based Fuzzy inference system approach as a novel method to estimate solubility of hydrocarbons in carbon dioxide

Recently due to increasing demand for energy and declination of oil reservoir the researchers have been encouraged to investigate the enhancement of oil recovery (EOR) approaches. One of popular and wide applicable processes in EOR is carbon dioxide injection which is attractive for researchers and industries due to environmentally aspects, good efficiency in displacement and low cost. The carbon dioxide injection causes the hydrocarbons extracted from crude oil so the solubility of hydrocarbon in carbon dioxide which is one of the critical parameters affects this phenomenon becomes interesting topic for researchers. In the present work Grid partitioning based Fuzzy inference system approach as a new method for prediction of solubility of hydrocarbons in carbon dioxide as function of temperature, pressure and carbon number of alkane was applied. To show the accuracy of the model the coefficients of determination were determined as 0.9902 and 0.9584 for training and testing phases respectively.     

VISCOSITY PREDICTION OF CARBON DIOXIDE + HYDROCARBON MIXTURES USING THE FRICTION THEORY

ABSTRACT

The general one-parameter f-theory model has been used in conjunction with the SRK and the PR EOS to predict the viscosity of well-defined carbon dioxide + hydrocarbon mixtures. The predicted viscosities are within the uncertainty appropriate for most industrial applications. Although the studied mixtures are simple representations of real oil mixtures with carbon dioxide, the f-theory approach can easily be extended to more complex scenarios, such as the simulation of carbon dioxide enhance oil recovery. Additionally, a comparison with the LBC model, which is a widely used model in the oil industry, has been carried out. In contrast to the f-theory models, the strong dependency that the LBC model has on the accuracy of the density is clearly evident for the kind of mixtures studied in this work. Furthermore, it is shown how the phase behavior complexity that carbon dioxide + hydrocarbon mixtures develop may have a direct influence on the performance of the viscosity modeling and prediction.     

Application of Fuzzy C-means algorithm as a novel approach to predict solubility of hydrocarbons in carbon dioxide

In the recent years, declination of oil reservoir causes the importance of researches on enhancement of oil recovery processes become more important. One of wide applicable approaches in enhancement of oil recovery is carbon dioxide injection which becomes interested because of relative low cost, good displacement and environmentally aspects. The injection of carbon dioxide to oil reservoir causes the lighter hydrocarbons of crude oil are extracted by CO₂. This phenomena can be affected by various factors such the solubility of hydrocarbons in carbon dioxide so in the present investigation Fuzzy c-means (FCM) as a novel approach for estimation of solubility of alkanes in carbon dioxide in terms of temperature, pressure and carbon number of alkane were utilized. The predicting algorithm FCM has reliable ability to estimate solubility based on graphical and statistical results. The coefficient of determination (R²) for training and testing data are calculated as 0.9856 and 0.9529 respectively.     

On the prediction of solubility of alkane in carbon dioxide using the LSSVM algorithm

The increasing global energy demand and declination of oil reservoir in recent years cause the researchers attention focus on the enhancement of oil recovery approaches. One of the extensive applicable methods for enhancement of oil recovery, which has great efficiency and environmental benefits, is carbon dioxide injection. The CO₂ injection has various effects on the reservoir fluid, which causes enhancement of recovery. One of these effects is extraction of lighter components of crude oil, which straightly depends on solubility of hydrocarbons in carbon dioxide. In order to better understand of this parameter, in this study, Least squares support vector machine (LSSVM) algorithm was developed as a novel predictive tool to estimate solubility of alkane in CO₂ as function of carbon number of alkane, carbon dioxide density, pressure, and temperature. The predicting model outputs were compared with the extracted experimental solubility from literature statistically and graphically. The comparison showed the great ability and high accuracy of developed model in prediction of solubility.     

The ANFIS-PSO strategy as a novel method to predict interfacial tension of hydrocarbons and brine

The interfacial tension of hydrocarbons and brine is known as one of the important parameters which are measured in petroleum and petrochemical industries for example the interfacial tension has straight effect on trapping of oil in a reservoir. In the present work the Adaptive neuro-fuzzy inference system (ANFIS) algorithm was used as a novel approach for estimation of interfacial tension between hydrocarbons and brine as function of pressure, temperature, carbon number of hydrocarbon and ionic strength of brine then the particle swarm optimization (PSO) was used to optimize the predicting model parameters.in order to better evaluation of performance of predicting algorithm the coefficient of determination (R²), average absolute relative deviation (AARD) and root mean squared error (RMSE) were estimated for different steps. The outcomes of this investigation expressed that proposed model has high potential for prediction of interfacial tension between hydrocarbons and brine.     

Experimental investigation of interfacial tension and oil swelling for asphaltenic crude oil/carbonated water system

The phenomenon of oil swelling at the oil-carbonated water (CW) system could be an important mechanism during the water alternating gas (WAG) injection process. Nevertheless, the study of the main mechanisms during water flooding (WF) is a complex topic that has not been well revealed so far, especially for asphaltenic crude oil (ACO) systems. Hence, the main goal of this experimental work is to determine the influence of carbon dioxide (CO₂) within the water phase in the interfacial tension (IFT) between water and crude oil for an extensive range of pressures between 400 psi and 2000 psi (i.e. 2.76–13.79 MPa), under two temperatures of 313.15 and 323.15 K (i.e. 40 and 50 °C) by axisymmetric drop shape analysis (ADSA) method. The experimental results demonstrate that the water/ CW and crude oil IFTs decline with time. The value of dynamic IFT (DIFT) between CW and crude oil decreased about 6 mN/m in comparison with the oil–water DIFT. As a result of the CO₂ solubility, the crude oil droplet swells with increasing pressure. When the temperature rises, the effects of increasing entropy phenomena and decline of liquids density is dominant compared to the solubility of CO₂. Thus, the volume of oil droplet increases with temperature, unexpectedly. In addition, as thetemperature increases the water/CW-Oil IFT is slightly reduced over a wide range of pressure evaluated. Nevertheless, there is a slight increase as the pressure increases for the water–oil system. According to the predicted results, interfacial tension of the CW-oil system declines with increasing pressure until the solubility of CO₂ is reached to a maximum value and then approximately remains changeless.     

Modeling of CO2-brine interfacial tension: Application to enhanced oil recovery

Development of reliable and accurate models to estimate carbon dioxide–brine interfacial tension (IFT) is necessary, since its experimental measurement is time-consuming and requires expensive experimental apparatus as well as complicated interpretation procedure. In the current study, feed forward artificial neural network is used for estimation of CO₂–brine IFT based on data from published literature which consists of a number of carbon dioxide–brine interfacial tension data covering broad ranges of temperature, total salinity, mole fractions of impure components and pressure. Trial-and-error method is utilized to optimize the artificial neural network topology in order to enhance its capability of generalization. The results showed that there is good agreement between experimental values and modeling results. Comparison of the empirical correlations with the proposed model suggests that the current model can predict the CO₂–brine IFT more accurately and robustly.     

超临界二氧化碳萃取泥页岩中可动油实验研究

针对泥页岩基质型页岩油有效开发面临的问题，以渤海湾盆地济阳坳陷沾化凹陷古近系沙河街组沙三下亚段典型含油泥页岩样品为例，开展了地层温度下密闭压裂改造后一定负压和超临界二氧化碳流体在不同压力条件下吞吐萃取可动油实验研究。实验结果揭示:地层温度与一定负压下仅微量轻质组分（15）可流动；在相同地层温度与流体压力下，随着超临界二氧化碳流体吞吐作用时间增长，可动油主要组分的碳数呈现增高趋势；随着流体压力的增大，超临界二氧化碳吞吐萃取可动油量显著提高，不仅以游离态赋存的轻质油组分和中质油组分被有效萃取出来，而且部分以束缚态（吸附—互溶态）赋存的重质组分也被萃取出来。超临界二氧化碳技术在提高泥页岩基质型页岩油采收率领域具有广阔的应用前景。      

哈尔金混合气藏成因及气体的垂向分布规律

松辽盆地长岭断陷哈尔金气藏（营城组）是烃类（CH₄）与CO₂混合气藏。对哈尔金混合气藏成因、气源特征及成藏期次研究表明,该混合气藏是烃类先于CO₂注入,气藏定型于新近纪,属晚期成藏。根据天然气在气藏中浮力与毛细管力平衡的物理模型,推演出烃类气藏含气饱和度分布的数学模型,该模型反映了储层中原始含气饱和度分布的基本原理。混合气藏中各种气体垂向分布符合重力分异原理,在均匀介质储层条件下,从气藏的底部向顶部,密度较大的CO₂饱和度逐渐降低,而密度相对较小的CH₄饱和度逐渐增加。在构造混合气藏的多孔介质储层中,密度大的CO₂气占据较大的孔隙部分,密度相对较小的CH₄气占据中等孔隙部分,而束缚水占据较小的孔隙部分。哈尔金混合气藏顶部CO₂含量高于底部是由于储层孔隙结构的变化所致。     

Feasibility of Gas Drive in Fang-48 Fault Block Oil Reservoir

The Fang-48 fault block oil reservoir is an extremely low permeability reservoir, and it is difficult to produce such a reservoir by waterflooding. Laboratory analysis of reservoir oil shows that the minimum miscibility pressure for CO2 drive in Fang-48 fault block oil reservoir is 29 MPa, lower than the formation fracture pressure of 34 MPa, so the displacement mechanism is miscible drive. The threshold pressure gradient for gas injection is less than that for waterflooding, and the recovery by gas drive is higher than waterflooding. Furthermore, the threshold pressure gradient for carbon dioxide injection is smaller than that for hydrocarbon gas, and the oil recovery by carbon dioxide drive is higher than that by hydrocarbon gas displacement, so carbon dioxide drive is recommended for the development of the Fang-48 fault block oil reservoir.     

A generalized set of correlations for plus fraction characterization

The importance of accurate determination of the critical properties of plus fractions in prediction of phase behaviour of hydrocarbon mixtures by equations of state is well known in the petroleum industry. It has been stated in various papers (Elsharkawy, 2001) that using the plus fraction as a single group in equation of state calculations reduces the accuracy of the results. However in this work it has been shown that using the proper values of critical temperature and pressure for the plus fraction group can estimate the properties of hydrocarbon mixtures, and they are accurate enough to be used in reservoir engineering and enhanced oil recovery calculations. In this paper, a new method is proposed for calculating the critical properties of plus fractions of petroleum fluids. One can use this method either in predicting critical pressure and temperature of single carbon numbers (SCNs) after the splitting process or in predicting critical pressure and temperature of the plus fraction as a single group. A comparison study is performed against Riazi-Daubert correlation (Riazi and Daubert, 1987) and Sancet correlations (Sancet, 2007) for 25 oil samples taken from 14 fields from southwest Iran. The results indicate the superiority of the proposed method to the Riazi-Daubert and Sancet correlations.     

胜利原油活性组分对其与烷基苯磺酸盐溶液组成的体系油-水界面张力的影响

 采用柱色谱四组分分离方法(SARA)对胜利原油进行分离,依次得到沥青质、饱和分、芳香分和胶质;采用碱醇液法萃取胜利原油得到其酸性组分,测定了各原油活性组分模拟油与烷基苯磺酸盐配制的系列标准溶液组成体系的油-水界面张力。结果表明,原油的酸性组分在低质量分数时通过改变油相的等效烷烃碳数（EACN）影响体系的油-水界面张力;高质量分数时则与表面活性剂混合吸附,使得体系油-水界面张力大幅度升高。胶质对其模拟油-表面活性剂标准溶液体系的油-水界面张力的影响与酸性组分的规律一致;沥青质的界面活性弱于酸性组分,混合吸附能力较弱,高质量分数时使体系油-水界面张力小幅度升高;饱和分和芳香分只影响油相的性质。     

基于摩尔密度的原油-CO2体系膨胀能力预测方法

通过对中国不同油区原油进行组分组成分析,选取了11种具有代表性的烃组分,分别与CO₂组成二元体系（共44个）进行恒质膨胀实验。所选取的原油烃组分包含碳原子数为6~16的直链烷烃、单环/双环环烷烃和单环/双环芳烃。对比分析实验结果发现,原油烃组分中溶解的CO₂可使体系发生一定程度的体积膨胀,且其膨胀幅度受原油组分自身性质影响较大,进一步提出用烃组分摩尔密度概念加以表征,膨胀系数与对应条件下原油组分的摩尔密度为递增直线关系,并建立了基于烃组分摩尔密度的CO₂-烃组分体系体积膨胀系数的预测方法。通过借鉴长庆油田D井和X井原油的加CO₂膨胀实验数据,验证了预测方法同样适用于对原油-CO₂体系膨胀的计算。结果表明,CO₂对原油的膨胀作用主要源于原油中烃组分的贡献,而非烃组分贡献较小。     

Viscosity Model for Petroleum Gases

The production processes for petroleum gases employ a broad range of simulation packages to reduce capital, time, and cost associated with actual recovery and pipeline transportation. Viscosity model is an important component of these packages. In this work, we have presented an empirical model for predicting the viscosity of petroleum gases, developed from the three-parameter Yaws equation. New constants were derived for various petroleum gases, as well as for gaseous carbon dioxide. Results obtained with the new model were compared with the viscosity predictions from the Yaws model, and the Miadonye and Clyburn correlation. For four petroleum gases and carbon dioxide at temperatures of 110 K to 1,500 K, the model gave an excellent viscosity prediction with overall average absolute deviations of 0.34% and 0.98%, respectively. The model is simple to incorporate into design and simulation packages, and more accurate than any correlation currently used in petroleum industry for predicting the viscosities of petroleum gases.     

油气层产能预测方法及模型

应用渗流力学基本理论,提出了以油气层有效渗透率为突破口,确立了利用常规测井资料在油气层测试之前对其产能进行预测的方法。根据塔里木盆地多个油气田大量试油和岩心分析资料,分别建立了油层和气层产能预测评价的实用数学模型,从而把渗流力学理论模型变成了易于应用的实用技术。应用所建立的产能预测方法及数学模型,在塔里木盆地第一次成功地对一口油气井的产能进行了定量预测计算。从日产天然气几十万方的特高产层到低产层和干层,塔里木盆地库车地区A井天然气层产能预测结果得到了完井测试结果的证实,表明了产能预测方法和模型的有效性,同时也说明利用测井资料预测评价油气层产能技术的良好应用前景。     

南海北部边缘盆地CO2成因及充注驱油的石油地质意义

在获取大量地质、地球化学资料的基础上,对南海北部边缘盆地CO₂的成因及充注驱油特征进行了分析,结果表明:南海北部边缘盆地CO₂可划分为壳源型岩石化学成因、壳源型有机成因、壳幔混合型及火山幔源型成因4种成因类型;其中,莺歌海盆地壳源型及壳幔混合型CO₂运聚成藏主要受控于泥底辟热流体晚期分层分块多期局部上侵活动与上新统—中新统海相含钙砂泥岩的物理化学综合作用;琼东南盆地东部及珠江口盆地火山幔源型CO₂成藏主要受控于幔源型火山活动及沟通深部气源的基底深大断裂的展布;CO₂运聚成藏中,其充注驱油过程主要受运聚输导条件及气源供给等诸多地质关键因素的制约和控制.由于CO₂充注驱替往往导致油气藏中油气再分配或重新组合,并引起原来的油气产出及产状特征发生变化,故容易形成新的油藏或气藏.因此,可以将CO₂充注驱油特征作为判识油气成藏动态过程的示踪标志,用于预测油气运聚状态,追踪油气分布特征.