Modeling of viscosity for mixtures of Athabasca bitumen and heavy n-alkane with LSSVM algorithm

The resources of heavy oil and bitumen are more than those of conventional light crude oil in the world. Diluting the bitumen with liquid solvent can decrease viscosity and increase the empty space between molecules. Tetradecane is a candidate as liquid solvent to dilute the bitumen. Owning to the sensitivity of enhanced oil recovery process, the accurate approximation for the viscosity of aforementioned mixture is important to decrease uncertainty. The aim of this study was to develop an effective relation between the viscosity of Athabasca bitumen and heavy n-alkane mixtures based on temperature, pressure, and weight percentage of n-tetradecane using the least square support vector machine. This computational model was compared with the previous developed correlation and its accuracy was confirmed. The value of R² and MSE obtained 1.00 and 1.02 for this model, respectively. This developed predictive tool can be applied as an accurate estimation for any mixture of heavy oil with liquid solvent.     

Data-driven modeling of heavy oil viscosity in the reservoir from geophysical well logs

Viscosity is the most crucial fluid property on recovery and productivity of hydrocarbon reservoirs, more particularly heavy oil reservoirs. In heavy and extra heavy oil reservoirs e.g. bitumen and tar sands more energy is required to be injected into the system in order to decrease the viscosity to make the flow easier. Therefore, attempt to develop a reliable and rapid method for accurate estimation of heavy oil viscosity is inevitable. In this study, a predictive model for estimating of heavy oil viscosity is proposed, utilizing geophysical well logs data including gamma ray, neutron porosity, density porosity, resistivity logs, spontaneous potential as well as P-wave velocity and S-wave velocity and their ratio (Vp/Vs). To this end, a supervised machine learning algorithm, namely least square support vector machine (LSSVM), has been employed for modeling, and a dataset was provided from well logs data in a Canadian heavy oil reservoir, the Athabasca North area. The results indicate that the predicted viscosity values are in agreement with the actual data with correlation coefficient (R²) of 0.84. Furthermore, the outlier detection analysis conducted shows that only one data point is out of the applicability of domain of the develop model.     

Applying FCM-ANFIS algorithm as a novel computational method for prediction of viscosity of bitumen and heavy alkane mixture

Recently the studies expressed that the noticeable number of oil reservoirs in all over the world are heavy oil and bitumen reservoirs. So the importance of enhancement of oil recovery (EOR) processes for heavy oil and bitumen reservoirs is highlighted. The Dilution of the reservoir fluid by solvents such as tetradecane is one of well-known methods for these types of reservoirs which effects oil recovery by decreasing viscosity. In the present study, Fuzzy c-means (FCM) algorithm was coupled with Adaptive neuro-fuzzy inference system (ANFIS) to predict viscosity of bitumen and tetradecane in terms of temperature, pressure and weight percent of tetradecane. The coefficients of determination for training and testing steps were calculated such as 0.9914 and 0.9613. The comparison of results and experimental data expressed that FCM-ANFIS algorithm has great potential for estimation of viscosity of bitumen and tetradecane.     

用RBF神经网络建立航空煤油密度软测量模型

在航空煤油的生产过程中,由于航空煤油密度的检测有很大的滞后而难以实现直接质量控制,降低了航空煤油产品的质量和产量,影响了生产效益。针对实际的生产过程,利用径向基函数（ＲＢＦ）神经网络开发了一个航空煤油密度的软测量模型,并用遗忘因子递失算法（ＲＦＦ）对其做了实时自适应校正,实现了航空煤油密度的在线估计,从而为进一步实现质量控制提供了有力的软测量工具。     

随机称量-煤油稀释-U形振动管法测定重稠油密度

从3个方面对重稠油的煤油混合稀释密度测定法进行改进。采用随机称量的煤油和重稠油混合稀释替代其等体积混合稀释,以U形振动管密度法替代手工漂浮密度计法,推导出以质量计量混合的重稠油密度计算公式,并编成程序安装到计算机里,直接输入4个参数值,即可迅速地得到重稠油标准密度值;重稠油与煤油混合近似服从体积加和性规则,以质量比1:1为中心点,在（1:2）~（2:1）的范围内重稠油与煤油稀释均匀,注入U形振荡管密度仪测定混合油的密度,可获得满意的重稠油密度结果;随机称量-煤油稀释-U形振动管法也适合含泡沫重质原油的密度测定;随机称量-煤油稀释-U形振动管法的3次密度测定的标准偏差在0.000 1 g/cm3以下,与直接U形振荡管法的平行性相当,优于漂浮密度计法。     

Variation of heavy oil composition during thermolysis with the addition of kerosene fraction of hydrocracking in flow reactor

Thermolysis experiments of heavy oil have been carried out in flow reactor with the addition of 2.5–5.0% kerosene fraction of hydrocracking of vacuum gas oil prepared from conventional oil. It has been shown that the additive increases the degree of heavy oil conversion, restricts coke formation in the products of thermolysis as compared to the process without the additive, and provides a decrease in the viscosity of the products. Thermolysis conditions with the addition of kerosene fraction in flow reactor, which give liquid products in more than 97% yield and the viscosity of less than 75?mm²/s, have been determined in the case of the heavy oil of Ashal’ chinskoe deposit.     

聚合物类型对油水界面性质影响研究

采用平面张力仪和表面粘弹性仪研究了部分水解聚丙烯酰胺（HPAM）和疏水缔合聚合物（HAP）对油水界面性质的影响研究结果表明,疏水缔合聚合物HAP可以显著降低煤油／模拟水体系界面张力,但对模拟油／模拟水体系界面张力影响较小;部分水解聚丙烯酰胺类聚合物CA对煤油／模拟水和模拟油／模拟水体系界面张力无明显影响。无论是煤油／模拟水体系还是模拟油／模拟水体系．随聚合物溶液浓度的增加,部分水解聚丙烯酰胺类聚合物CA均使体系界面剪切粘度逐渐增大。而疏水缔合聚合物HAP均使体系的界面剪切粘度先增加,后降低,而后再增加。部分水解聚丙烯酰胺（HPAM）相对分子质量较大时,HPAM溶液／模拟油体系的界面剪切粘度较大。     

Viscosity estimation of mixed oil using RBF-ANN approach

Diluting the bitumen and heavy oil with a liquid solvent such as tetradecane is one way to decrease the viscosity. The accurate estimation for the viscosity of the aforesaid mixture is serious due to the sensitivity of enhanced oil recovery method. The main aim of this study was to propose an impressive relation between the viscosity of heavy n-alkane and Athabasca bitumen mixtures based on pressure, temperature, and the weight percentage of n-tetradecane using radial basis function artificial neural network (RBF-ANN). Also, this model has been compared with previous equations and its major accuracy was evidenced to estimate the viscosity. The amounts of mean relative error (MRE %) and R-squared received 0.32 and 1.00, respectively. The endeavors confirmed amazing forecasting skill of RBF-ANN for the approximation of the viscosity as a function of temperature, pressure, and the weight percentage of n-tetradecane.     

Evolving ANFIS model to estimate density of bitumen-tetradecane mixtures

Recent investigations have proved more worldwide availability of heavy crude oil resources such as bitumen than those with conventional crude oil. Diluting the bitumen through injection of solvents including tetradecane into such reservoirs to decrease the density and viscosity of bitumen has been found to be an efficient enhanced oil recovery approach. This study focuses on introducing an effective and robust density predictive method for Athabasca bitumen-tetradecane mixtures against pressure, temperature and solvent weight percent through implementation of adaptive neuro-fuzzy interference system technique. The emerged results of proposed model were compared to experimentally reported and correlation-based density values in different conditions. Values of 0.003805 and 1.00 were achieved for mean square error and R², respectively. The developed model is therefore regarded as a highly appropriate tool for the purpose of bitumen-tetradecane mixture density estimation.     

THERMAL EFFECT ON THE PERMANENT VISCOSITY OF A SASKATCHEWAN HEAVY OIL

Future energy demands will likely cause increased activity towards the recovery of heavy oil using non-conventional means. Most non-conventional attempts to recover Saskatchewan's heavy oil resources have utilized thermal techniques.

This report discusses the permanent viscosity changes which occur when heavy oil.is subjected to thermal processes from 220 to 425°C. It was observed that under closed operating conditions, the oil viscosity drops in a manner which can be modeled by a first order, kinetic reaction model. The rate constant for this reaction varied from 0.3 × 10^-3 to 6.0 × 10^-3 h^-1 depending on temperature and the assumed molecular weights of the model components. These findings closely parallel earlier results.

Experimental observations on thermal effects during opened operating conditions indicate a dramatic and rapid rise In the remaining crude oil viscosity. The oil was observed to increase its permanent absolute viscosity by a factor as high as 21 times its original absolute viscosity. The single product, first order kinetic model was not capable of predicting this rise in oil viscosity. A simple, two product, first order kinetic model was developed and found to fit the data satisfactorily with a rate constant of 0.6 h^-1 for heavy product formation and a rate constant of 0.03 h^-1 for light product formation at 275°C.     

A process neural network model for calculation of heavy oil viscosity in high water cut stage

The commonly used heavy oil viscosity models are just for low water cut stage, this paper determined the influencing factors of the viscosity model in high water cut stage, by analyzing the viscosity data, presents a new and simple method base on the Process Neural Network in high water cut stage to predict the viscosity of heavy oil, which can valid measure the viscosity of heavy oil by Input parameters of the different temperature, water cut and API. Compared with the real data, the new model has the small computation error and the reliability by the process neural network new model for predicting oil viscosity. it can be tested in practices in calculating the viscosity of similar oilfields in high water cut stage.     

THERMAL EFFECT ON THE PERMANENT VISCOSITY OF A SASKATCHEWAN HEAVY OIL

ABSTRACT

Future energy demands will likely cause increased activity towards the recovery of heavy oil using non-conventional means. Most non-conventional attempts to recover Saskatchewan's heavy oil resources have utilized thermal techniques.

This report discusses the permanent viscosity changes which occur when heavy oil.is subjected to thermal processes from 220 to 425°C. It was observed that under closed operating conditions, the oil viscosity drops in a manner which can be modeled by a first order, kinetic reaction model. The rate constant for this reaction varied from 0.3 × 10^?3 to 6.0 × 10^?3 h^?1 depending on temperature and the assumed molecular weights of the model components. These findings closely parallel earlier results.

Experimental observations on thermal effects during opened operating conditions indicate a dramatic and rapid rise In the remaining crude oil viscosity. The oil was observed to increase its permanent absolute viscosity by a factor as high as 21 times its original absolute viscosity. The single product, first order kinetic model was not capable of predicting this rise in oil viscosity. A simple, two product, first order kinetic model was developed and found to fit the data satisfactorily with a rate constant of 0.6 h^?1 for heavy product formation and a rate constant of 0.03?h^?1 for light product formation at 275°C.     

高黏度牛顿流体中砂粒运动实验研究

为了解决疏松砂岩稠油油藏开采过程中的砂粒运移问题,基于固液两相流理论及实验流体力学理论,利用研制的高黏度流体颗粒自由沉降实验装置,分别开展了高黏度牛顿流体中球形颗粒的自由沉降实验、砂粒的自由沉降实验和不同含砂体积分数下混合黏度变化规律实验,根据实验结果,建立了不同粒径范围的阻力系数模型以及混合黏度模型。研究表明,高黏油混合黏度存在相应的临界值,在实验条件下,含砂体积分数为0.1%时,混合黏度达到最低,不利于携砂。结合上述模型和实验结果,建立了高黏度牛顿流体中的砂粒运动模型,揭示了高黏介质中颗粒的特定运移规律,为今后稠油携砂流动规律理论研究奠定了基础。      

减压蜡油加氢裂化催化剂组合动力学模型

以高压加氢裂化六集总动力学模型为基础,建立预测催化剂组合体系产品分布的数学模型。按固定馏程间隔将原料油和加氢裂化生成油划分为减压蜡油 加氢裂化尾油（>360℃）、柴油馏分（290～360℃）、喷气燃料馏分（175～290℃）、重石脑油馏分（65～175℃）、轻石脑油馏分（<65℃）和炼厂气（C4-）6个集总。分别以2种不同类型加氢裂化催化剂的实验数据为基础,采用Matlab 2011b数值计算软件和非线性最小二乘法对动力学模型参数进行了优化回归。以优化回归后的动力学模型参数为初值,调整部分模型参数,建立了预测催化剂组合体系产品分布的数学模型。用该模型计算得到的加氢裂化产品分布与实验值之间的一致性较好,其偏差均小于2%。     

胜利油田稠油未动用储量评价及动用对策

胜利油田稠油资源丰富,经过多年技术攻关和开发建设,仍有近3.20×108t探明储量未得到有效动用.为实现不同类型稠油未动用储量的有效开发,系统分析了储量特点及开发难点,将其划分为敏感稠油、深层低渗稠油、特超稠油、边底水稠油和超薄层稠油5种类型,综合应用物理模拟、数值模拟、室内实验等方法,制订了不同类型未动用储量的开发对...     

塔河油田超深井井筒掺稀降粘技术研究

基于热量传递原理和两相流动理论,建立了井筒掺稀油降粘工艺中产液沿井筒流动与传热的热力学模型。计算了产液沿井筒的温度分布和压力分布,同时进行了不同掺稀条件下降粘的室内实验。运用该模型结合实验结果对塔河油田稠油井掺稀降粘效果进行了计算,分析了不同工艺参数对掺稀降粘效果的影响。结果表明,井筒掺稀油降粘工艺适合于含水率低于20%的油井,开式掺稀油反循环比开式掺稀油正循环生产更有利于提高降粘效果,塔河油田井筒掺稀降粘合理的掺稀比率为1:2至1:1。     

超稠油油藏水平井蒸汽吞吐开发合理界限研究

为在经济、技术允许的条件下，合理地开发超稠油油藏，增加可利用的储量资源。本文应用物理模拟，数值模拟方法，研究了达9块超稠油的流体组成，粘温关系和油藏渗流特征，以及超稠油水平井蒸汽吞吐注汽工艺参数对开采效果的影响，考虑经济因素的影响，建立蒸汽吞吐开采目标优化函数，提出开发超稠油油藏的合理工作制度和技术经济界限，研究认为，在实施超稠油水平井蒸汽吞吐时，应采取注高温，高干度，高强度的蒸汽和短周期，多周次的工作制度，同时在完井方式和注采工艺等方面采取配套措施，才能取得一定的效益，研究结果为该类油藏的热采开发提供了可靠的依据。     

超稠油燃烧基础参数特征研究

针对超稠油油藏开展火烧油层技术可行性研究的需要,利用自行设计研制的火烧油层物理模拟实验装置,分别采用超稠油、特稠油、普通稠油开展了火烧油层燃烧基础参数物理模拟实验。对比了不同类型稠油门槛温度、燃料消耗量等燃烧基础参数,结合产出油组分及温度场发育特征,分析了超稠油燃烧基础参数特征。研究认为,超稠油油藏开展火烧油层试验是可行的,超稠油门槛温度、燃料消耗量等燃烧基础参数值均高于其他类型稠油;稠油火烧油层的驱油效率与黏度相关,黏度越大其燃料消耗量越大,其最终的驱油效率相对较低;火烧后原油性质发生了明显改善。     

裂缝性稠油油藏蒸汽吞吐动态预测模型

在常规稠油油藏蒸汽吞吐动态预测模型的基础上,运用能量平衡理论,建立了裂缝性稠油油藏蒸汽吞吐开采动态预测模型,给出了热膨胀作用、重力驱替和毛管渗吸作用下的分析解。在求解过程中,考虑了温度对粘度、含油饱和度和油水相对渗透率的影响。该模型的实例计算结果与数值模拟计算结果相近,这表明该模型能用于裂缝性稠油油藏蒸汽吞吐动态预测。     

重质船用燃料油黏度预测模型研究

研究了现有黏度预测模型应用于重质船用燃料油黏度预测的可行性,筛选几种常见的黏度物理模型,进行试验数据对比和最优模型选取,基于重质船用燃料油数据库对Cragoe模型进行修正,并结合掺稀降黏试验数据分析混合机制对预测模型相对误差的影响。结果表明,针对目前市场上常用的重质船用燃料油调合组分,采用Cragoe黏度模型进行预测误差较小。这是由于Cragoe黏度模型的预测不受组分油黏度比的限制,在重质船用燃料油中的适用性最好。采用所提出的修正模型,可进一步降低对重质船用燃料油黏度预测的误差。分析多组分调合的结果显示,若组分中的黏度呈梯度分布时可降低预测误差。另外,渣油与稀组分油（简称稀油）调合时,沥青质的络合效应在一定程度上会影响模型的预测准确性。