三塘湖油田油气藏流体PVT特征

油气藏流体主要由烃类成分组成,在油藏及地面条件下,这种烃类混合物的物理性质取决于其化学成分及给定的温度和压力;油气藏地层流体PVT分析技术是一项用于判断油气藏类型、计算油气储量,提供在油气藏工程计算中有关数模、初步采收率研究、采供气方案设计和石油天然气深加工等基本数据的应用技术。初步总结和归纳三塘湖油田油气藏地层流体PVT、地层多级脱气、粘度与压力变化关系等特征,并结合油田的实际情况,为三塘湖油田编制科学的开发方案提供了依据。     

油气藏流体粘度预测模型的对比分析

为了优选出精度较高的粘度预测模型，提高油气藏流体粘度预测的准确性，总结了油气工业中常用的LBC粘度模型、CS粘度模型、LLS粘度模型、PT粘度模型和PR粘度模型。应用各种粘度模型分析对比纯组分、组分明确的二元混合物、油气藏流体及其注CO2等多种体系的粘度预测精度。对比结果表明：对纯组分粘度预测时，PR粘度模型总平均误差最小；对CH4-C3H8和CH4-nC4H10二元体系粘度预测时，CS粘度模型效果最佳；对CH4-nC10H22二元体系粘度预测时，PT粘度模型总平均误差最小；对凝析气和油藏原油粘度预测时，PR粘度模型预测精度最佳；对注CO2原油体系粘度预测时，PR粘度模型总平均误差最小。推荐使用PR粘度模型计算油气藏流体的粘度。     

统一粘度模型预测天然气粘度

本文采用基于对应态原理的统一粘度模型对天然气气相和液相的粘度进行了预测。模型通过引入混合规则,仅需天然气各组分的基本物性数据即可预测混合物粘度,计算简便可行。应用于二元轻烃混合物７４９个数据点及天然气高压粘度的预测,平均绝对差分别为４．１３％和２．１３％,优于现有的其它粘度模型。     

柴达木盆地南八仙油气田油气相态特征

柴达木盆地南八仙油气田发育两套含油气层系：深层的E₃¹油气藏和浅层的N₂²-N₁油气藏。7个样品的油、气高压物性实验结果揭示了凝析油气藏的相态特征和相态分布规律。浅层油气藏属于饱和烃类体系，油气藏类型包括油藏、油环凝析气藏、凝析气顶油藏和凝析气藏；深层油气藏属于未饱和烃类体系，油气藏类型包括湿气藏、凝析气藏和油藏。研究表明，由于深、浅气藏流体组分及温度、压力条件不同，烃类相态也就不同。深入了解地下流体相态特征并确定油气藏类型对于高效合理地开发油气藏、提高油、气采收率具有重要的意义。     

偏差因子在富含CO2天然气井计算中应用

井筒压力、温度分布在气井的日常管理及气井设计、动态分析中足两个重要的参数,直接用烃类气井压力、温度模型计算富含CO2气井的压力温度,因CO2的性质和烃类差异较大导致计算结果不准确.为此,通过针对富含CO2气修正相应偏差因子,考虑CO2性质影响,基于质量、动量、能量守恒原理及传热学理论,建立预测井筒流体压力、温度分布的数学模型,进行井筒计算.通过计算,分析不同CO2含量情况下偏差因子、压力、温度及密度变化井筒中天然气相态变化情况,得出同一深度时压力随CO2含量的增加而变小,温度随深度增加趋于气藏温度,沿片筒向井口流速增高,向地层传热减少,井口温度增高,井口差异较大,在井口密度接近液相,即密度较大,越到井底密度越小,总的有从液相向气相过渡的趋势.     

用链状流体分子热力学模型计算常压流体混合物的黏度

在Eyring绝对速率理论的基础上,结合链状流体分子热力学模型,建立了一个常压流体混合物的黏度方程。该黏度方程的关键是采用了链状流体分子热力学模型,同时计算黏度方程中的压缩因子和过剩Gibbs自由能。对选择的若干常压二组分流体混合物的黏度数据的计算结果表明,采用一个与温度无关的可调参数时,黏度方程能关联二组分流体混合物的黏度数据,平均相对误差为3.18%;采用两个与温度无关的可调参数时,关联效果大幅度的改善,平均相对误差降至1.22%;采用与温度有关的可调参数时,单参数和双参数所得黏度计算结果的平均相对误差分别为2.79%和0.84%。实验结果表明,该黏度方程的预测结果稍差,误差一般为7%左右。因此,实际计算中,推荐使用与温度无关的可调参数的黏度模型。     

渤海湾盆地廊固凹陷杨税务潜山油气成藏的流体包裹体证据

廊固凹陷杨税务潜山凝析油天然气藏是近年来渤海湾盆地的重要勘探发现,但对其油气成藏过程的认识还不够深入。通过对潜山储层中的流体包裹体进行岩相学、显微测温、拉曼光谱和PVT模拟等分析表明,储层中主要发育5类流体包裹体,即气液两相盐水包裹体、气体包裹体、气液两相烃类包裹体、气液两相富气包裹体和单液相烃类包裹体。杨税务潜山主要发育2期油气成藏过程,以晚期天然气充注为主。第1期烃类包裹体主要发蓝绿色荧光,单相烃类包裹体较多,气液两相烃类包裹体较少,共生盐水包裹体较少,未见含甲烷的富气包裹体,以原油充注为主,形成于东营组沉积期成藏阶段。第2期烃类包裹体以气液两相富气包裹体为主,其气液比相对较大,液相发蓝色荧光,为高成熟度凝析油,富气包裹体中含甲烷等气体,说明成藏的天然气为湿气,成藏期为明化镇组沉积期至今。成藏期的古压力计算表明,第1期成藏的压力系数在1.00~1.17,属于正常压力油气藏;第2期成藏的压力系数相对较高,在0.95~1.33,总体属于正常压力范围,个别井区达到异常高压范围（压力系数大于1.20）。杨税务潜山油气藏的古今压力系数变化不大,表明其封闭性较好,有利于油气保存。     

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气藏烃类流体储存于地下的多孔介质中，部分烃类气体会吸附在多孔介质固体表面，从而会应影响气藏储量计算、气井产能计算、气藏开发动态分析等气藏工程计算的准确性。文章分析、研究了烃类气体在储层多孔介质表面上的吸附机理，应用F—HVSM模型，通过实例计算，得到了烃类气体混合物在储层多孔介质表面的吸附情况。研究表明：烃类气体混合物在储层孔隙介质中的吸附量随温度的升高而减少，随压力的升高而增大；在同一孔隙介质中，当温度、压力相同时，重组分含量相对较高的烃类气体体系，其吸附量相应较大；对同一烃类气体体系而言，在相同的温度、压力下，孔隙介质渗透率越低，比表面越大，其吸附量也越大；烃类气体混合物在中、低渗储层多孔介质表面的吸附量的数量级为10-2mol/kg。     

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对油气藏所具有的原始烃类孔隙体积一般采用容积法进行计算，在油气藏开发早期各种资料有限的情况下，计算结果精度较低。由于ＰＶＴ等组分膨胀实验模拟了油气藏的开采动态，在压力下降引起地下流体膨胀量等于地面采出量乘以流体体积系数的情况下，实验所取得的相对体积随压力的变化关系能有效地用于烃类孔隙体积的预测。这就提供了一种无须考虑有效厚度、孔隙度和含油（气）饱和度这３个难以确定的参数，而在油气藏的早期评价时便可计算出其原始烃类孔隙体积的可能性。该方法的精确度，取决于实验样品的代表性和实验本身的准确性，以及生产动态资料的可靠性。由于现在油气藏的开发早期都要进行ＰＶＴ测试，为该方法应用提供了条件。应用实例表明该方法简便、可靠。     

石油-稀释剂混合物粘度及质量分数模型的建立

粘度模型是提高石油采收率程序包的重要组成部分，并且对纯原油而言，已经建立了几个精确模型。在这篇文献中，深化了一种在早期出版物中出现的简单相关模型，不仅能预测稀释原油混合物的粘度，而且还能对使原油粘度降低到可开采粘度的稀释剂的质量分数进行预测，在深化粘度模型中，把纯原油及稀释剂的粘度作为边界点，并建立质量分数的n(粘度降低参数）次幂，用来解释随稀释剂质量分数的增加原油粘度急剧降低的程度，模型以三种不同原油及5种不同稀释剂的99个数据点为基础而建立，粘度范围为10^-1-106mm^2/s，模型重新计算了粘度及质量分数值，并且与Cragoe,Chirinosde等方程进行了对比，结果吻合程度非常高，粘度与质量分数的平均绝对偏差分别为12％和5％，对深化模型以外的预测数据表明实验数据与预测数值有很好的吻合性，25℃，60．3℃和82．6℃混合物的粘度平均绝对偏差在10％以下。     

Prediction of thermodynamic properties of natural gas mixtures using 10 equations of state including a new cubic two-constant equation of state

In this contribution, 10 equations of state (EoSs) are used to predict the thermo-physical properties of natural gas mixtures. One of the EoSs is proposed in this work. This EoS is obtained by matching the critical fugacity coefficient of the EoS to the critical fugacity coefficient of methane. Special attention is given to the supercritical behavior of methane as it is the major component of natural gas mixtures and almost always supercritical at reservoir and surface conditions. As a result, the proposed EoS accurately predicts the supercritical fugacity of methane for wide ranges of temperature and pressure. Using the van der Waals mixing rules with zero binary interaction parameters, the proposed EoS predicts the compressibility factors and speeds of sound data of natural gas mixtures with best accuracy among the other EoSs. The average absolute error was found to be 0.47% for predicting the compressibility factors and 0.70% for the speeds of sound data. The proposed EoS was also used to predict thermal and equilibrium properties. For predicting isobaric heat capacity, Joule–Thomson coefficient, dew points and flash yields of natural gas mixtures, the predictive accuracy of the EoS is comparable to the predictive accuracy of the Redlich–Kwong–Soave (RKS) EoS or one of its variants. For predicting saturated liquid density of LNG mixtures, however, the accuracy of predictions is between the RKS and Peng–Robinson (PR) EoSs.     

Estimation of Low-Pressure Gas Viscosity

A new correlation has been developed to estimate viscosity for gases at low pressure for several organic and inorganic compounds as a function of temperature depending on critical properties, as well as, critical density, acentric factor, and molecular weight. The proposed correlation has been verified using data of approximately 72 data points, and it is shows a significantly better correlation with an average absolute error of 2.0.     

油气燃料临界燃烧和超临界燃烧的基础理论

利用热力学基本定律和常用状态方程vdW EOS、RK EOS、SRK EOS、PR EOS及PG EOS，对CH₄、C₂H₆、C₃H₈、C₈H₁₈、C₁₆H₃₄等的临界温度、临界压力、临界比容三者之间的相互关系进行了理论计算。还计算了He、H₂、N₂、O₂、CO、CO₂和水蒸气等的物性参数，并分别与其临界试验参数进行了对比分析。结果表明：理想气体状态方程PG EOS得到的结果最差，它对这些物质临界压力计算的平均误差高达260%以上，对临界温度计算的平均误差也在72%以上；而PR EOS得到的结果最好，且对临界温度计算的平均误差不足0.2%，最大误差在0.6%以下，对临界压力计算的平均误差则小于0.8%；其余EOS对物质临界参数的描述则介于二者之间。其中：vdW EOS对这些物质临界压力描述的最大误差在60%以上，平均误差在14%以上；RK EOS和SRK EOS对临界压力描述的最大误差均在13%以上，平均误差在2.6%左右，但SRK EOS对临界温度的总体描述要好于RK EOS。因此，PG EOS不能用来表述这些物质临界参数之间的相互关系，而PR EOS和SRK EOS可对这些物质临界参数之间的相互关系作出相对较好的描述。     

高压液体混合物粘度的自由体积模型

在液体粘度的自由体积模型的基础上,引用Carnaban Starling的自由体积表达式,得到关联或预测高压下液体混合物粘度的模型。对简单非极性体系仅需纯物质参数;对非水体系需一个混合参数;对含水体系,需两个混合参数。对14个体系(其中5个含水体系),共1637个数据点的总平均关联误差仅为2.1％。考虑到同时关联温度、压力和组成影响的复杂性,这一结果是相当令人满意的。     

Viscosity Model for CO-Noble Gas Mixtures at Atmospheric Conditions

We have presented an empirical model for predicting the viscosity of binary mixtures of gases, based on our previous correlation for natural gas mixtures (Miadonye and Clyburn, 2003). New parameters were derived for binary mixtures of carbon monoxide-noble gases and for carbon monoxide-nitrogen gases, for temperatures from 0-1000°C. The model was validated with the Chapman-Enskog equation for gas mixtures, with and without length scaling factor. For five equimolar and eight non-equimolar mixtures of gases at temperatures from 0-1000°C, the model gave an excellent viscosity prediction with an overall average absolute deviation of 0.45%. The model is simple to incorporate in design and simulation packages, and more accurate than any correlation currently used for estimating the viscosities of gas mixtures.     

Improved neural-network model predicts dewpoint pressure of retrograde gases

Accurate prediction of dewpoint pressure is a critical element in reservoir-engineering calculations. The objective of this paper is to present a novel and highly accurate application of the neural-network model (NNM) to predict dewpoint pressures in retrograde gas reservoirs. We were able to demonstrate that the model described in this paper is more accurate than any presented to date. In addition, the model is simple and is able to duplicate with reasonable accuracy the temperature–dewpoint pressure behavior of constant-composition gas condensate fluids.The neural-network model was developed using a set of 802 experimental constant volume depletion (CVD) data points. To train the neural-network model, a set of 641 experimental data points of CVD for different gas condensate fluids was used. The model was tested with 161 experimental data points, not used during the training process, to prove its accuracy. The study also considered a detailed comparison between the results predicted by this more efficient neural-network model and those predicted by other correlations for estimating dewpoint pressure of retrograde gas. The performance of this improved neural-network model and available correlations was evaluated versus the Peng–Robinson Equation of State (PR-EOS) model for the same reservoir fluid composition, a gas condensate from the Cusiana Field, in Colombia.This improved neural-network model was able to predict the dewpoint pressure with an average absolute error of 8.74%, as a function of temperature, hydrocarbons and non-hydrocarbon compositions, molecular weight, and specific gravity of heptanes-plus fraction. Neural-network models can save calculation time in the prediction of the dewpoint pressures with more reliability than available multiple-regression techniques.     

A CORRELATION FOR VISCOSITY AND SOLVENT MASS FRACTION OF BITUMEN-DILUENT MIXTURES

A simple equation is presented for predicting the kinematic viscosity of bitumens and heavy oils mixed with diluents. The correlation has been shown to provide accurate viscosity estimates of these mixtures for a wide range of data and requires only the knowledge of the pure bitumen and pure solvent viscosities at any given temperature.

The correlation makes use of a viscosity reduction parameter which reduces error significantly when compared to similar equations presented by Chirinos et al. (1983), and Cragoe (1933). For a total of 89 data points, excluding the pure bitumens and diluents values, the correlation yielded an overall average absolute deviation of about 14 percent. The same equation was applied to predict the mass fraction of diluent required to reduce bitumen viscosity to pumping viscosity. Predicted values matched experimental values very well, with an overall average absolute deviation of about 6 percent.     

气藏气体体积系数计算公式的修正

介绍了石油工程中气藏气体体积系数的计算公式,分析了计算公式的不足,提出了计算公式的修正方法。采用石油工程中常用的PREOS和Gopal(1977)法计算偏差系数,利用修正的计算公式对10个甲烷摩尔分数为50.56%～97.75%的气藏气体体积系数进行了计算并与实验值比较,其平均相对误差分别为1.325%和3.173%;而石油工程中现有的计算公式对这10个相同气藏气体体积系数计算的平均相对误差分别为1.867%和3.701%。结果表明,修正的计算公式提高了计算精度。     

页岩中基于孔隙度和有机碳含量的甲烷吸附量计算

根据四川盆地页岩气勘探开发经验,页岩气在地下的赋存状态为吸附态和游离态,以及少量溶解态,其中游离态的含量可达20％～85％.因此,研究地层高压条件下页岩中甲烷吸附特征对页岩储层的准确评价以及储量预测具有重要意义.以四川地区页岩气储层为对象进行等温吸附实验,分析实验结果后发现,甲烷在页岩孔隙中随压力增加其吸附量逐渐增加,...     

A new model for predicting the viscosity of heavy oil

Models of heavy oil viscosity are considered as a function of temperature and API gravity in literature at home and abroad, and the API gravity is measured under the condition of 20°C, but without considering the effect of temperature on the API and colloid asphalt content in heavy oil. A total of 197 data points from 41 samples of Huabei oilfield are tested from previous studiesand five other heavy oil viscosity models, and the calculation value is much less than the measured value. The new prediction model of heavy oil viscosity is established by Simplex Method and the General Global Optimization Method. The new model takes into account the temperature, API, colloid asphalt, and the influence of temperature on API. Using the new model to predict the viscosity of heavy oil samples of Huabei oilfield, the average relative error of 3.36%, mean absolute error of 7.61%. The new prediction model is precise than other eight models, calculated values and measured values coincide when degree is higher.