Application of LSSVM algorithm as a novel tool for prediction of density of bitumen and heavy n-alkane mixture

The most of oil reservoirs in the world are heavy oil and bitumen reservoirs. Due to high viscosity and density of these types of reservoirs the production has problems so importance of enhanced oil recovery (EOR) processes for them is clear. The injection of solvents such as tetradecane is known as one of methods which improve oil recovery from bitumen reservoirs. In the present investigation, the Least squares support vector machine (LSSVM) algorithm was used to estimate density of Athabasca bitumen and heavy n-alkane mixture in term of temperature, pressure and weight percent of the solvent. The Root mean square error (RMSE), average absolute relative deviation (AARD) and the coefficient of determination (R²) for total dataset are determined 0.033466, 0.0025686 and 1 respectively. The predicted results indicate that the LSSVM algorithm has potential to be a predicting machine for the bitumen-heavy alkane mixture density prediction.     

Application of fuzzy c-means algorithm as a novel method to predict density of mixtures of Athabasca bitumen and heavy n-alkane

The significant number of oil reservoir are bitumen and heavy oil. One of the approaches to enhance oil recovery of these types of reservoir is dilution of reservoir oil by injection of a solvent such as tetradecane into the reservoirs to modify viscosity and density of reservoir fluids. In this investigation, an effective and robust estimating algorithm based on fuzzy c-means (FCM) algorithm was developed to predict density of mixtures of Athabasca bitumen and heavy n-alkane as function of temperature, pressure and weight percent of the solvent. The model outputs were compared to experimental data from literature in different conditions. The coefficients of determination for training and testing datasets are 0.9989 and 0.9988. The comparisons showed that the proposed model can be an applicable tool for predicting density of mixtures of bitumen and heavy n-alkane.     

Application of Grid partitioning based Fuzzy inference system and ANFIS as novel approach for modeling of Athabasca bitumen and tetradecane mixture viscosity

The heavy oil and bitumen reservoirs have effective role on supplying energy due to their availability in the world. The bitumen has extremely high viscosity so this type of reservoirs has numerous problems in production and trans- portation.one of the common approach for reduction of viscosity is injection of solvents such as tetradecane. In the present study the Grid partitioning based Fuzzy inference system was coupled with ANFIS to propose a novel algorithm for prediction of bitumen and tetradecane mixture viscosity in terms of pressure, temperature and weight fraction of the tetradecane. In the present study, the coefficients of determination for training and testing phases are determined as 0.9819 and 0.9525 respectively and the models are visualized and compared with experimental data in literature. According to the results the predicting method has acceptable accuracy for prediction of bitumen and tetradecane mixture viscosity.     

PSO-ANFIS modeling of viscosity for mixtures of Athabasca bitumen and a high-boiling n-alkane

The bitumen and heavy oil reservoirs are more in number than light crude oil reservoirs in the world. To increase the empty space between molecules and decrease viscosity, the bitumen was diluted with a liquid solvent such as tetradecane. Due to the sensitivity of enhanced oil recovery process, the accurate approximation for the viscosity of mentioned mixture is important. The purpose of this study was to develop an effective relation between the viscosity of Athabasca bitumen and heavy n-alkane mixtures based on pressure, temperature, and the weight percentage of n-tetradecane using the adaptive neuro-fuzzy inference system method. For this model, the value of MRE and R² was obtained as 0.34% and 1.00, respectively; so this model can be applied as an accurate approximation for any mixture of heavy oil with a liquid solvent.     

Modeling of the density of mixtures of Athabasca bitumen and a high boiling n-alkane

Recent studies revealed the more availability of heavy oil resources, such as bitumen than other types. So, the injection of solvents such as tetradecane with the aim of diluting bitumen is applied as an enhanced oil recovery (EOR) method for such reservoirs. This study has investigated the prediction of density for Athabasca bitumen–tetradecane mixture, under different temperature, pressure, and solvent's weight percent conditions, using a radial basis function neural network (RBF-NN) technique. Results were then compared with experimental values and values reported based on the previous correlation. MSE and R² values were 0.10496 and 1.00, respectively. Thus, this proposed model has been introduced as a very appropriate model for density prediction of bitumen–tetradecane mixture.     

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.     

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.     

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.     

注有机极性气体开采稠油技术

针对储层薄、埋藏深的稠油油藏开发难度大及开采成本高的问题,在注溶剂萃取稠油技术（VAPEX）的基础上,提出向稠油油藏注入一种低饱和蒸汽压的有机极性气体PE提高采收率的采油技术。利用相态观察及黏度测定两种方法研究了该气体与稠油在气态和液态两种情况下的降黏效果。另外,还对比了该气体与VAPEX中常用烃类气体LPG对稠油降黏及萃取效果。实验结果表明:该气体在原油中具有很好的溶解性,能大幅度降低原油黏度;和LPG与稠油作用不同,该有机气体在液态状态下能大量分散稠油中的沥青,形成一种低密度低黏度混合相。现场应用表明注入该气体的措施井增产效果明显,因此,该注气技术可适用于一些埋藏深、油层薄、渗透性差和黏度高的稠油油藏。     

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...     

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.     

稠油开采技术现状与发展方向初步探讨

世界上已发现的原油资源中,稠油储量占比超过2/3。由于稠油黏度高、流动性差,开采难度大,对技术要求高。针对中国稠油油藏类型多和深度变化大的特点,分析总结了国内外稠油开采现状和提高开采效率的主要技术方向,结合现场生产动态,探讨了现有商业化开采技术对不同类型稠油油藏的适应性、应用潜力和面临的主要技术挑战。研究结果表明,蒸汽吞吐仍然是中国稠油开采的主要方式,但大部分油田已经进入到蒸汽吞吐开采的末期,开采效率低,目前成熟接替技术(蒸汽驱、SAGD和火烧油层)的适用油藏范围有限,急需研发新的接替技术;中深层稠油开发技术系列较为成熟,但针对深层、超深层和复杂类型(如裂缝/溶洞性)稠油油藏的提高采收率技术尚不成熟,加强井下产生蒸汽、溶剂辅助、原位改质和气化等前沿技术的研究更具现实意义和应用前景。研究成果对拓展稠油开采技术研究领域和方向具有借鉴和指导作用。     

超稠油油藏注氮气辅助蒸汽吞吐数模研究

超稠油油藏蒸汽吞吐后期,油汽比低,周期含水量高,开采效果差,注氮气辅助蒸汽吞吐已经成为1种提高超稠油开采效果的有效手段。与常规稠油油藏不同,超稠油对于注氮参数以及筛选条件有更严格的限制。模拟研究得知,超稠油油藏蒸汽吞吐不同时期宜采用不同的注氮模式。因该类型油藏对原油黏度、油层厚度、采出程度敏感性较强,从而确定超稠油油藏选井标准。该研究对同类油藏注氮辅助蒸汽吞吐开采具有指导意义。     

超深层低渗透稠油CO2增溶降黏体系研发与应用

目前采用CO2吞吐开采超深层低渗透稠油油藏无法达到经济产能,即使使用降黏剂和CO2辅助热采开发也存在降黏效果差、单井产能低和油藏开发难度大等问题。通过使用分子体系设计和实验验证,对CO2增溶降黏剂进行研发,利用化学剂本身降黏功能和增强CO2溶解原油的能力,将化学降黏与CO2开采两种稠油开采技术进行有机结合,达到降低稠油黏度以及增强稠油流动性的目的。研制的增溶降黏剂可使CO2溶解度增大7倍,降黏率高达99.2%,有效解决了超深层低渗透稠油降黏范围小的难题。矿场应用结果表明,试验井周期平均日产油量达到原日产油量的2.5倍以上,取得了良好的效果。     

海上高含水稠油油藏治理对策研究

为探究海上高含水稠油油藏的治理方法,针对海上稠油油藏储层物性好、水利能量充足、单井产液量高、原油黏度高和油井高含水等特点,通过建立井组地质模型,采用油藏数值模拟方法,研究不同驱替开采方式和注采参数对抑制、延缓底水锥进的效果.研究发现:(1)针对海上稠油油藏的敏感性分析,注入介质＞地层倾角＞渗透率韵律＞级差;(2)0.2...     

草古1潜山裂缝性碳酸盐岩稠油油藏开采特征

方法：结合矿场实践,应用油藏工程方法,研究裂缝性古潜山碳酸盐岩特,超稠油油藏开采特征及开采效果。目的：搞清此类油藏的开采规律,使其得到合理,有效的开发,结果：高角度裂缝发育的古潜山碳酸盐岩特,超稠油油藏,由于裂缝发育的随机性,直井开采效果差异较大,而水平井能钻遇更多的裂缝,故水平井方式开发较有利;由于此类油藏渗透性较好,依据平面原油性质的变化,采取井筒降粘和蒸汽吞吐引效相结合的配套开采模式,并应用改进的井筒降粘采油工艺及优化的注采参数等一系列配套措施,对改善开采效果均是有效的。结论：通过开采特征研究,所确定的布井方式,开采方式及采取的一系列参数技术界限,保证了此类特种油藏的经济,有效开发。     

Heavy oil and bitumen recovery by hot solvent injection

Thermal and miscible methods are commonly used for in situ recovery of heavy oil and bitumen. Both techniques have their own limitations and benefits. However, these methods can be combined by co-injecting solvent with steam or injecting solvent into a pre-heated reservoir. The current work was undertaken to study the performance of solvents at higher temperatures for heavy oil/bitumen recovery. Glass bead packs and Berea sandstone cores were used in the experiments to represent different types of pore structures, porosity and permeability. After saturating with heavy oil, the samples were exposed to the vapor of paraffinic solvents (propane and butane) at a temperature above the boiling point of the solvent, and a constant pressure of 1500 kPa. A mechanical convection oven was used to maintain constant temperature across the setup. The setup was designed in such a way that a reasonably long sample (up to 30 cm) can be tested to analyze the gravity effect. The oil recovered from each of these experiments was collected using a specifically designed collection system and analyzed for composition, viscosity and asphaltene content.The final amount of oil recovered in each case (recovery factor but not extraction rate) was also analyzed and the quantity and nature of asphaltene precipitated with each of the tested solvents under the prevailing temperature and pressure of the experiment was reported. Optimal conditions for each solvent type were identified for the highest ultimate recovery. It was observed that recovery decreased with increasing temperature and pressure of the system for both solvents, and that the best results were found when experimental temperature is only slightly higher than the saturation temperature of the solvent used. It was also noticed that butane diluted the oil more than propane which resulted in lower asphaltene content and viscosity of oil produced with butane as a solvent.     

稠油油藏水平井热采应用研究

本文油藏数值模拟技术研究了不同类型稠油油藏水平井注蒸汽开采的可行性、相应的开采方式及油层厚度与原油粘度对水平井注蒸汽开采效果的影响。     

稠油油藏水驱转热采工艺可行性分析

胜利油区水驱稠油油藏经过多年的注水开发，由于原油粘度的增大和剩余油分布的高度分散，导致开发后期采油速度低、产量低，严重限制了油藏采收率的提高，因而必须寻找合适的接替技术。中外水驱转热采的实践经验证明，热采技术的高温蒸汽降粘和超覆作用等手段可大幅度提高水驱后稠油油藏的采收率，解决水驱开发无法解决的降低原油粘度和提高波及体积等问题。水驱转热采适合于埋藏深度小于1400m的普通稠油油藏，可大幅度提高原油采出程度。胜利油区的油藏条件完全符合水驱后转注蒸汽的筛选标准，具有广阔的应用前景。     

辽河油田稠油油藏氮气泡沫驱适应性研究

目前辽河油 田大多数普通稠油油藏都已经历了蒸汽吞吐开采个别原油粘度较低的稠油油藏.在经历了短暂的常规干抽后转入了水驱开发。由 于大多数稠油油藏非均质性较强,经过吞吐开采。其剖面及平面动用矛盾十分突出,加之压力大幅度下降、原油脱气粘度升高.使开发效果明显变差。在这种条件下,开展了氮气泡沫驱提高采收率的机理研究,研究结果认为.采用氮气泡沫驱可以改善上述油藏的开发效果。应用数 值模拟及物理模拟方法首次较为系统地对氮气泡沫驱的适应性进行了研究总结出适合氮气泡沫驱开采的油藏条件.并在此基础上筛选出辽河油田6个适合实施氮气泡沫驱的稠油区块。