Modelling of CO2 separation from gas streams emissions in the oil and gas industries

Sodium glycinate solutions have low vapor pressure, low viscosity, and high chemical reactivity with CO₂. They have remarkable potential for removal of CO₂ from the flue gases, because absorption of CO₂ with an amino acid salt solution such as sodium glycinate is accompanied with precipitation. As the liquid phase contains solid compounds during absorption of CO₂, the reactions move into the production of various materials and a further amount of CO₂ is absorbed. In the current study, a support vector machine algorithm is utilized to predict carbon dioxide solubility in aqueous sodium glycinate solutions over wide ranges of temperature, pressure, and concentration. The proposed model can be of immense value for engineers to have a quick check on the CO₂ solubility in sodium glycinate solutions without opting for any experimental works. Results obtained from the model have shown excellent agreement with reported data in the literature.     

Hybrid connectionist model determines CO2–oil swelling factor

In-depth understanding of interactions between crude oil and CO₂ provides insight into the CO₂-based enhanced oil recovery (EOR) process design and simulation. When CO₂ contacts crude oil, the dissolution process takes place. This phenomenon results in the oil swelling, which depends on the temperature, pressure, and composition of the oil. The residual oil saturation in a CO₂-based EOR process is inversely proportional to the oil swelling factor. Hence, it is important to estimate this influential parameter with high precision. The current study suggests the predictive model based on the least-squares support vector machine (LS-SVM) to calculate the CO₂–oil swelling factor. A genetic algorithm is used to optimize hyperparameters (γ and σr2) of the LS-SVM model. This model showed a high coefficient of determination (R² = 0.9953) and a low value for the mean-squared error (MSE = 0.0003) based on the available experimental data while estimating the CO₂–oil swelling factor. It was found that LS-SVM is a straightforward and accurate method to determine the CO₂–oil swelling factor with negligible uncertainty. This method can be incorporated in commercial reservoir simulators to include the effect of the CO₂–oil swelling factor when adequate experimental data are not available.     

一种改进的LS-SVM算法及其应用

 为了避免LS-SVM算法中存在的矩阵求逆问题,提出一种改进的LS-SVM算法,即利用改进PSO算法对LS-SVM算法中线性方程组进行迭代优化求解,这样既能加快算法训练速度和节省内存,又总能得到最小二乘解,提高计算精度。将此改进算法应用到长庆气田C井目的层井段进行气层识别,并与BP神经网络算法、经典的SVM算法和传统的LS-SVM算法比较,结果表明此算法识别精度高,收敛速度快,与试气结果吻合,效果显著。     

聚合物驱含水率变化定量表征模型

基于实际聚合物驱含水率变化曲线的特征及定量表征模型的建立原则,建立聚合物驱含水率变化定量表征模型。刻画聚合物驱含水率变化曲线特征的主要参数包括含水率开始下降时注入聚合物孔隙体积倍数、含水率下降最大值时注入聚合物孔隙体积倍数、含水率恢复到初始注聚含水率值时对应的注入聚合物孔隙体积倍数及含水率下降最大值。基于粒子群优化算法给出了定量表征模型参数的拟合方法。同时,综合油藏数值模拟、正交设计和支持向量机方法建立了考虑多因素组合影响的聚合物驱含水率变化特征参数预测模型。计算实例表明,应用所建立的模型,能便捷地预测聚合物驱含水率变化,把握其整体趋势,指导聚合物驱方案的实施。图3表3参16     

Prediction of the viscosity of water-in-oil emulsions

Water-in-oil (W/O) emulsions occur in different parts of petroleum recovery and since their properties are different from those of crude oil and water, it is essential to find information about their physical properties. The water causes some problems and heightens the cost of oil production. Separation of water from oil, water treatment, and disposal of water are the steps that make oil production costly. Among various physical properties of W/O emulsions, viscosity is the most important. Because of droplet crowding, emulsions indicate non-Newtonian behavior, which is why their viscosity is higher than the viscosity of oil and water. As a result, it is essential to have knowledge about the viscosity of W/O emulsions in order to extract and process petroleum properly. In this study, an intelligent model, namely a COA-LSSVM, is presented to accurately prognosticate the viscosity of W/O emulsions. The results indicated that the values estimated by the developed model is in great consistency with the laboratory data by R² of 0.9972 and MSE of 1.1762 * 10^?5. A comparison with another model, which was recently introduced, revealed that the developed model in this study is superior.     

Modelling of methane hydrate formation pressure in the presence of different inhibitors

Formation of gas hydrates is one of the problems in the production, processing, and transportation of natural gas. Hence, an understanding of conditions where hydrates form is necessary to overcome hydrate-related issues. The aim of this study was to develop an effective relation between the methane hydrate formation pressure based on the temperature, weight fraction of inhibitor, and molecular weight of inhibitor using the least square support vector machine. This computational model indicates the great ability of predictions for determining hydrate pressure in the presence of different inhibitors such as the methanol, ethylene glycol, diethylene glycol, and triethylene glycol. The values of R-squared (R²) and mean squared error obtained for this model are 0.9925 and 0.2325, respectively. This developed predictive tool can be applied as an accurate estimation of methane hydrate formation pressure.     

地层可钻性级值预测新方法

对测井资料与地层可钻性级值的关系进行了分析,提出了一种基于粒子群优化支持向量机算法预测地层可钻性级值的新方法,利用测井声波时差、地层密度、泥质质量分数和地层深度进行学习训练支持向量机,并利用粒子群优化算法对支持向量机(PSO-SVM)参数进行优化,建立了预测地层可钻性级值的支持向量机模型。应用该方法对准噶尔盆地庄2井的地层可钻性级值进行了预测,并将该方法的预测结果与BP神经网络方法的预测结果进行了比较。结果表明,该方法优于BP神经网络方法,具有预测精度高、收敛速度快、推广能力强等优点。     

不同压力下冻土区水合物法封存CO2的实验研究

目的水合物法封存CO₂稳定性良好、储气密度高,是一种极具潜力的碳封存方式,利用冻土区的地层条件更具独特优势,将CO₂气体注入冻土区地层中,在一定的温度和压力条件下,形成固态CO₂水合物实现封存。 方法依据国内冻土地区地层深度对应的温度和压力条件,选取不同地层深度(150 m和200 m)对应温度(1.27 ℃和2.72 ℃)和有效孔隙含水率(40%),研究不同注气压力(3.5 MPa、4.5 MPa和5.5 MPa)下的封存特征。分析封存过程的温度和压力变化、封存速率、最终水转化率和最终封存率等动力学规律。 结果封存压力越高,水合物法封存所需的诱导时间越短,压力降幅越大。较高的封存压力导致初期封存速率较慢,缓慢封存期的持续时间减少,且封存压力越高,封存率、最终水转化率和水合物相饱和度越高。封存温度越高,压力对封存率的影响效果越明显。 结论在地层深度150 m(对应地层平均温度1.27 ℃)、5.5 MPa及有效孔隙含水率(40%)的条件下,CO₂封存效果最佳。      

超临界含杂质CO2管网停输再启动对水合物生成的影响

目的 在CO₂管道输送过程中,输送管网的工艺参数在停输再启动工况下会发生变化,容易生成水合物堵塞管网。预测和分析CO₂输送过程中水合物及水含量的生成规律,以保证管输的效率和安全性。方法 基于延长油田一期36×10⁴ t/a输送管网设计,利用OLGA软件建立超临界含杂质CO₂输送管网模型,在稳态输送下水合物生成的研究基础上,预测和分析停输再启动工况对水合物的生成位置和生成量等的影响。结果 在停输工况下,水合物生成温度和压力降低,水合物生成位置发生前移,在管网入口5 km处开始有质量浓度为0.18 kg/m³的水合物生成;而在再启动工况下,水合物生成温度增加,水合物生成量峰值为0.9 kg/m³,水合物生成区段减短且生成位置后移至管网20 km处。结论 模拟结果可以较好地指导延长油田管网设计,为管网的安全运行提供理论依据。     

A new empirical correlation for prediction of carbon dioxide separation from different gas mixtures

Carbon dioxide (CO₂) emission from different systems such as fuel gas (H₂+CO₂), flue gas (N₂+CO₂), and biogas gas (CH₄+CO₂) is one of the main factors of global warming and environmental problems. So, CO₂ separation from different systems is essential. Low energy consumption, environmental friendliness, and low operational cost of hydrate-based gas separation (HBGS) process show the high potential of this approach in separation of some gases such as CO₂. Hydrate phase equilibrium data are required for designing the separation process. So far numerous models has been proposed for prediction of hydrate formation/dissociation conditions in various systems with/without promoters or inhibitors. This study attempts to present a simple and comprehensive model for fast prediction of hydrate formation conditions to separate CO₂ from biogas, fuel gas, and flue gas systems in the presence of promoters such as tetra-n-butylammonium bromide, tetra-n-butylammonium chloride, tetra-n-butylammonium fluoride, tetra-n-butyl ammonium nitrate, and tetra-n-butylphosphonium bromide. According to the error analysis results, this point can reach the new proposed correlation has better estimation capability in comparison with Sayyad Amin et al. model. On the other hand, hydrate formation temperature can be predicted in the presented correlation with high accuracy.     

Modelling of CO2 capture and separation from different gas mixtures using semiclathrate hydrates

In this work we present a model for predicting hydrate formation condition to separate carbon dioxide (CO₂) from different gas mixtures such as fuel gas (H₂+CO₂), flue gas (N₂+CO₂), and biogas gas (CH₄+CO₂) in the presence of different promoters such as tetra-n-butylammonium bromide (TBAB), tetra-n-butylammonium chloride (TBAC), tetra-n-butylammonium fluoride (TBAF), tetra-n-butyl ammonium nitrate (TBANO₃), and tetra-n-butylphosphonium bromide (TBPB). The proposed method was optimized by genetic algorithm. In the proposed model, hydrate formation pressure is a function of temperature and a new variable in term of Z, which used to cover different concentrations of studied systems. The study shows experimental data and predicted values are in acceptable agreement.     

基于粒子群优化最小二乘支持向量机的非线性AVO反演

为了求解非线性AVO反演问题,本文提出基于粒子群算法和最小二乘支持向量机的非线性AVO反演方法,并用粒子群算法优化最小二乘支持向量机的参数。即首先通过精确Zoeppritz方程正演得到角道集,并进行动校正和部分角度叠加;然后运用最小二乘支持向量机方法建立反射振幅与弹性参数之间的非线性模型;最后以此非线性模型对地震道集数据进行反演。模型数据和实际资料的反演结果表明,该方法克服了常规广义线性AVO反演在远炮检距及弹性参数纵向变化大等情况下的缺陷,可直接从实际地震道集数据中提取较高精度的地层弹性参数,具有快速稳健、抗噪能力强的优点。     

多元热流体热采井筛管腐蚀试验及分析

多元热流体热采技术是一种先进的多效热采技术,但多元热流体环境对井下系统腐蚀比其它稠油开采技术更为严重。针对注多元热流体井中筛管的腐蚀问题,选取构成金属棉筛管和复合筛管的TP100、3Cr、N80、316L金属网和434金属棉5种材料,模拟现场注热条件和生产条件,开展了腐蚀试验,进行了腐蚀速率预测,提出了多元热流体井的筛管选材和筛管防腐的建议。      

An Efficient Methodology for Performance Optimization and Uncertainty Analysis in a CO2 EOR Process

Abstract

A pragmatic technique is proposed and successfully applied to determine the optimal production–injection scheme in a CO₂ flooding reservoir under uncertainty. Well rates of injectors and bottomhole pressures of producers are chosen as the controlling variables. Geological uncertainty is accounted for using the multiple reservoir models. An objective function associated with net present value (NPV) is defined, and a modified genetic algorithm is employed to determine the optimal production–injection scheme. It is shown from a field case study that the optimized scheme can not only increase the expected oil recovery and NPV by 7.8 and 6.6%, respectively, but can also achieve a considerably small range of possible NPVs.     

粒子群算法在砂砾岩体岩性识别中的应用

针对砂砾岩地层岩性变化大、非均质性强、常规测井曲线影响因素多、砂砾岩地层地质特征与测井曲线呈现非线性关系等特点,采用支持向量机方法(SVM)对地层岩性进行划分。选用粒子群算法对支持向量机参数进行优化,得到岩性识别模型;根据模型对研究区的30多口井的岩性进行划分,取得良好的地质应用效果。     

含杂质CO2管道输送水合物形成规律研究

在延长油田产生的CO₂气体输送过程中,管线会发生水合物冰堵,影响气体输送流量,为了探究CO₂水合物在管道输送过程中的形成规律,利用PVTSIM软件生成了CO₂水合物的相平衡曲线,并通过OLGA软件对水平管和弯管输送的水合物形成规律进行了模拟分析。结果表明:在低温高压条件下,水平管和弯管输送过程中均会有水合物形成,其生成过程是一种类似于盐类的结晶过程,通常包括成核和生长两个阶段,然后依靠流体颗粒之间的黏附力致使水合物聚集,与直管段相比,弯管段更容易产生水合物;水合物生成速率均由小到大,然后快速进入稳定阶段,最后趋于0。现场管线的水合物也多发生在弯管处,从而进一步验证了CO₂水合物的形成规律。因此,在管道输送过程中应避免高压出口和低温入口条件,保证管道安全运营。 