原油沥青质初始沉淀压力测定与模型化计算

温度、压力及组成的改变均会造成原油中沥青质产生沉淀,导致储层伤害和井筒堵塞。文中通过自主研制的固相沉淀激光探测系统,用透光率法首次测定了伊朗南阿油田原油样品在不同温度下的沥青质初始沉淀压力;同时利用Nghiem等建立的沥青质沉淀预测的热力学模型对油样沥青质初始沉淀压力进行计算,并与实验结果拟合。结果表明：利用透光率法测定该油田油样,在44,80,123℃下的沥青质初始沉淀压力点分别为42.8,39.7,35.2 MPa;沥青质初始沉淀压力随着温度的升高,在井筒温度范围内呈线性关系。模型计算与实验结果误差不超过15%,所以利用Nghiem模型对原油沥青质的初始沉淀压力进行预测是可靠的。     

Experimental and modeling asphaltene precipitation in presence of DBSA using PC-SAFT EOS

Asphaltene precipitation and subsequent deposition in production tubing and topside facilities present significant cost penalties to crude oil production. Therefore, it is highly desirable to predict their phase behavior and the efficiency of dispersants in preventing or delaying deposition. Very few studies have been carried out on the molecular interactions between asphaltenes and different dispersants. As a result, the mechanisms by which dispersants stabilize asphaltenes are still open to discussion. The authors introduced a new method to characterize asphaltenes in perturbed chain statistical association fluid theory equation of state (EOS; perturbed-chain statistical association fluid theory EOS [PC-SAFT-EOS]) and correctly model the effect of dodecyl benzene sulfonic acid (DBSA) dispersant on the thermodynamic behavior of asphaltenes. Using the filtration method the effect of the ionic dispersant (DBSA) on asphaltene precipitation for different concentrations of n-heptane was measured experimentally, then modeled through PC-SAFT EOS. In the approach only the hard-chain and the dispersion terms are taken into consideration, and PC-SAFT parameters were calculated based on Gonzales et al. (2007 Gonzalez, D. L., Hirasaki, G. J., Creek, J., and Chapman, W. G. (2007). Modeling of asphaltene precipitation due to changes in composition using the perturbed chain statistical associating fluid theory equation of state. Energy Fuels 21:1231–1242.[Crossref], [Web of Science ®] , [Google Scholar]) based on molecular weight (Mw) and aromaticity factor (γ). Additionally, the model could correctly predict the amount of asphaltene precipitation upon addition of DBSA dispersant.     

Phase behavior modeling of asphaltene precipitation utilizing RBF-ANN approach

Precipitation of heavy hydrocarbons, particularly asphaltenes, is the reason for numerous operational and production problems in the petroleum industry. Hence, knowing the amount of asphaltene precipitation is a critical commission for petroleum engineers to overcome its problems. The aim of this study was to predict the amount of asphaltene precipitation as a function of temperature, dilution ratio, and molecular weight of different n-alkanes utilizing radial basis function artificial neural network (RBF-ANN). Additionally, this model has been compared with previous correlations, and its great accuracy was proved to predict the precipitated asphaltene. The values of R-squared and mean squared error obtained were 0.998 and 0.007, respectively. The efforts confirmed brilliant forecasting skill of RBF-ANN for the approximation of the precipitated asphaltene as a function of temperature, dilution ratio, and molecular weight of different n-alkanes.     

Experimental investigation of dodecylbenzene sulfonic acid and toluene dispersants on asphaltene precipitation of dead and live oil

Asphaltene is the heaviest fraction of oil, and if the thermodynamic conditions of oil change, it can be separated from oil precipitate. Of common methods for preventing asphaltene precipitation, using predictive methods, biological methods and injection of dispersants can be mentioned. In this study, the effect of two dispersants of toluene and dodecylbenzene sulfonic acid on asphaltene precipitation of a dead and a live oil sample has been investigated. According to the results, these dispersants in dead oil create an optimum point for asphaltene precipitation. In live oil, these dispersants reduce asphaltene precipitation down to 70%. In addition, it was observed that as an effect of injecting these dispersants, the average sizes of asphaltene flocculation have reduced.     

Determination of asphaltene precipitation using a CPA equation of state

Asphaltene precipitation during natural depletion and miscible gas injection is a common problem in oilfields throughout the world. Therefore, predicting asphaltene phase behavior through thermodynamic modeling may help to control its precipitation and reduce the associated problems. In this work, a new modified CPA equation of state (EoS) was used to model asphaltene precipitation. This equation is based on a combination of a new physical part and the Wertheim association term.

The results of the new model were compared with the experimental data of five oil samples. The results showed that this modified CPA EoS can predict asphaltene precipitation with good accuracy.     

Determination of asphaltene precipitation using a new CPA-based equation of state

Asphaltene precipitation during natural depletion and miscible gas injection is a common problem in oilfields throughout the world. Therefore, predicting asphaltene phase behavior through thermodynamic modeling may help to control its precipitation and reduce the associated problems. In this work, a new modified cubic-plus-association (CPA) equation of state (EoS) was used to model asphaltene precipitation. This equation is based on a combination of a new physical part and an association term.

The new physical part has an evolved cubic equation of state with a new attractive term in this work. The results of the new modification of CPA EoS were compared with the experimental data of two oil samples (oil samples 1 and 2) that belonged to Moradi et al. The results showed that this modified CPA EoS can predict asphaltene precipitation with good accuracy.     

An Experimental Investigation of Asphaltene Precipitation During Natural Production of Heavy and Light Oil Reservoirs: The Role of Pressure and Temperature

Abstract

Many oil reservoirs encounter asphaltene precipitation as a major problem during natural production. In spite of numerous experimental studies, the effect of temperature on asphaltene precipitation during pressure depletion at reservoir conditions is still obscure in the literature. To study their asphaltene precipitation behavior at different temperatures, two Iranian light and heavy live oil samples were selected. First, different screening criteria were applied to evaluate asphaltene instability of the selected reservoirs using pressure, volume, and temperature data. Then, a high pressure, high temperature filtration (HPHT) setup was designed to investigate the asphaltene precipitation behavior of the crude samples throughout the pressure depletion process. The performed HPHT tests at different temperature levels provided valuable data and illuminated the role of temperature on precipitation. In the final stage, the obtained data were fed into a commercial simulator for modeling and predicting purposes of asphaltene precipitation at different conditions. The results of the instability analysis illustrated precipitation possibilities for both reservoirs which are in agreement with the oil field observations. It is observed from experimental results that by increasing the temperature, the amount of precipitated asphaltene in light oil will increase, although it decreases precipitation for the heavy crude. The role of temperature is shown to be more significant for the light crude and more illuminated at lower pressures for both crude oils. The results of thermodynamic modeling proved reliable applicability of the software for predicting asphaltene precipitation under pressure depletion conditions. This study attempts to reveal the complicated role of temperature changes on asphaltene precipitation behavior for different reservoir crudes during natural production.     

The Impact of CO2 Injection and Pressure Changes on Asphaltene Molecular Weight Distribution in a Heavy Crude Oil: An Experimental Study

Abstract

This work concerns observing the pressure as well as CO₂ mole percentage effects on asphaltene molecular weight distributions at reservoir conditions. A high-pressure, high-temperature asphaltene measurement setup was applied, and the amount of precipitated asphaltene at different pressures as well as CO₂ mole percentage in an Iranian heavy crude oil was measured. Moreover, the asphaltene molecular weight distributions during titration of crude oil with different n-alkanes were investigated. The gel permeation chromatography (GPC) apparatus was used for characterization of asphaltene molecular weight under different conditions. It has been observed that some thermodynamic changes such as pressure depletion above the bubble point increase the average molecular weight of asphaltene and cause the asphaltene molecular weight distributions changes from a bimodal curve with two maxima to a single maxima curve. One the other hand, below the bubble point, pressure reduction causes a decrease in the average molecular weight of asphaltene and also causes the shape of asphaltene molecular weight distributions to restore, which might be due to dissolution of asphaltene aggregates. An interesting result is that asphaltene molecular weight distribution at the final step of pressure reduction tests, ambient condition, shows approximately the same trend as the distribution of asphaltene molecular weight obtained at reservoir condition. This behavior explains the reversibility of the asphaltene precipitation process under pressure depletion conditions. In the case of CO₂ injection, the graphs of asphaltene molecular weight distributions always show a single modal trend and shift toward larger molecular weight values when CO₂ mole percentage increases. The results of this work can be imported to thermodynamic models that use polydisperse data of heavy organic fractions to enhance their performance at reservoir conditions. The distributions obtained by this method are good indicators of asphaltene structures at reservoir conditions.     

Development of an Artificial Neural Network Algorithm for the Prediction of Asphaltene Precipitation

Abstract

The precipitation and deposition of crude oil polar fractions such as asphaltenes in petroleum reservoirs considerably reduce rock permeability and oil recovery. Therefore, it is of great importance to determine how and how much the asphaltenes precipitate as a function of pressure, temperature, and liquid phase composition. The authors designed and applied an Artificial Neural Network (ANN) model to predict the amount of asphaltene precipitation at a given operating condition. Among this training, the back-propagation learning algorithm with different training methods was used. The most suitable algorithm with an appropriate number of neurons in the hidden layer, which provides the minimum error, was found to be the Levenberg-Marquardt (LM) algorithm. An extensive experimental data for the amount of asphaltene precipitation at various temperatures (293–343 K) was used to create the input and target data for generating the ANN model. The predicted results of asphaltene precipitation from the ANN model was also compared with the results of proposed scaling equations in the literature. The results revealed that scaling equations cannot predict the amount of asphaltene precipitation adequately. With an acceptable quantitative and qualitative agreement between experimental data and predicted amount of asphaltene precipitation for all ranges of dilution ratio, solvent molecular weight and temperature was obtained through using ANN model.     

Solubility parameter of oils by several models and experimental oil compatibility data: implications for asphaltene stability

In a previous study we obtained reference values of solubility parameter of two Brazilian crude oils based on asphaltene flocculation data. In this work, these reference values were compared to those obtained by nine models available in the literature and oil compatibility data were experimentally obtained to enhance the modeling evaluation. These evaluations allowed to select models to predict asphaltene stability and oil compatibility. As a result, only our method along with three other methods can accurately predict the experimental results of the compatibility between oil mixtures, and the conclusion is that usually recommended models are not the best choice.     

高温高压热模拟装置的研制

高温高压热模拟装置是用于有机质生、排烃机理研究的专用仪器.由于目前国内外模拟装置不能满足有机质在高温高压下实验需求,为此研制了温度达600℃、压力为40MPa的模拟装置.该装置设计了两种规格、两种型式的高压釜,使用了动密封和静密封两种密封形式,解决了高温高压下釜的密封难题.在国内首次研制出无螺栓快速拆卸式高温高压反应釜.装置采用了中频感应加热技术,使样品加热温度更均匀、更精确(温差＜±5℃),同时增大了恒温区、缩短了预热时间;实现了100t液压机压力调控精度为±0.2MPa下恒压长达100h.实验全过程全微机控制与监测,产物接收系统实现自动计量,消除了人为误差.     

Prediction of Asphaltene Precipitation during Pressure Depletion and CO2 Injection for Heavy Crude

Abstract

In this work, a thermodynamic approach is used for modeling the phase behavior of asphaltene precipitation. The precipitated asphaltene phase is represented by an improved solid model, and the oil and gas phases are modeled with an equation of state. The Peng-Robinson equation of state (PR-EOS) was used to perform flash calculations. Then, the onset point and the amount of precipitated asphaltene were predicted. A computer code based on the solid model was developed and used for predicting asphaltene precipitation data reported in the literature as well as the experimental data obtained from high-pressure, high-temperature asphaltene precipitation experiments performed on Sarvak reservoir crude, one of Iranian heavy oil reserves, under pressure depletion and CO₂ injection conditions. The model parameters, obtained from sensitivity analysis, were applied in the thermodynamic model. It has been found that the solid model results describe the experimental data reasonably well under pressure depletion conditions. Also, a significant improvement has been observed in predicting the asphaltene precipitation data under gas injection conditions. In particular, for the maximum value of asphaltene precipitation and for the trend of the curve after the peak point, good agreement was observed, which could not be found in the available literature.     

The Application of an Artificial Neural Network (ANN) and a Genetic Programming Neural Network (GPNN) for the Modeling of Experimental Data of Slim Tube Permeability Reduction by Asphaltene Precipitation in Iranian Crude Oil Reservoirs

Abstract

The authors introduce a new implementation of the neural network (ANN), genetic programming neural network (GPNN), and neuro-fuzzy (NF) technology in petroleum engineering. An intelligent framework is developed for calculating the amount of permeability reduction by asphaltene precipitation in Iranian crude oil reservoirs over a wide pressure, temperature, and solvent mole fraction range. Theoretical results and practical experience indicate that a feed-forward network can approximate a wide class of function relationships very well. In this work, a conventional feed-forward multilayer ANN, GPNN, and NF approach have been proposed to predict the amount of permeability reduction. The accuracy of the method is evaluated by predicting the amount of permeability reduction of various reservoir fluids not used in the development of the models. One of the ways in modeling such systems is using intelligent techniques, which need information about the systems, so, based on some intelligent learning methods, it can provide a suitable model. Furthermore, the performance of the model is compared with the performance of a simple model for permeability reduction prediction, a new correlation, and experimental data. Results of this comparison show that the proposed GPNN method first and then NF method is superior both in accuracy and generality, over the other models.     

Investigation of the asphaltene deposition along the flow of the oil in tubing: Experimental study and a parametric analysis

In this work, a novel experimental setup was designed and utilized to carry out the n-alkane induced asphaltenes for understanding the kinetics of deposition and also effects of oil velocity, oil-precipitant volumetric dilution ratio, and temperature on the rate of asphaltene deposition. As the deposited layer of asphaltenes makes it difficult for the flow of oil along the tube, measurement of the pressure drop across the tube section of setup enabled the measurement of the amount and extent of deposition process at desired condition. The experimental results revealed that increasing the velocity of fluid across the pipe dominance the shear force on asphaltene deposit and cause remobilization of part of the deposit into the flowing fluid in contrary to oil-precipitant ratio, where deposition rate is enhanced with increasing DR ratio. The results of this work elucidate some less-addressed shadows of dynamics of flow blockage in pipelines and could create a better framework for conducting forthcoming experiments     

多功能高温高压三维比例物理模拟试验装置

根据辽河油田稠油地质开发特征及转换开发方式研究的需求，依据相似理论及先进的自动化测控技术，在室内建立了高温高压三维比例物理模型。经系统测试与系统功能评价，高温高压三雏比例物理模型的主要功能、测量与控制精度等符合设计指标要求。利用该试验装置开展了组合式蒸汽吞吐、蒸汽驱、SAGD等物理模拟试验研究。     

静35块浅层高凝油热水加溶剂驱油物模实验及其效果分析

沈阳油田静35块浅层高凝油油藏于1995年投入开发，目前开发矛盾十分突出，存在的主要问题是地层温度仅高出原油析蜡温度2－4℃，原油在采出过程中由于脱气造成油层内析蜡堵塞渗流通道，导致全块开发形势十分被动。本文主要针对区块当前的开发现状，从室内实验、开发实践入手，明确了以维持该块长期开发的两个必要条件，即保持地层温度高于原油析蜡温度及使地层压力尤其是近井地带压力高于或保持在饱和压力附近。为满足上述两项必要条件而开展了热水加溶剂的物模实验，其研究成果对静35块的全面开发具有重要意义，对同类油田的开发也具有一定的参考价值。     

原油和烃源岩中高分子量烷烃的气相色谱定量分析

介绍了HP6890气相色谱仪测定高分子量烷烃的定量方法,测定高分子量烷烃的碳数可达100,用正构烷烃标样nC20,nC30,nC40,nC50,nC60以及nC4D50作为内标对原油和烃源岩中低含量的高分子量烷烃进行定量。测得nC60的质量校正因子约为nC30的83%。对南阳油田3个典型高蜡原油样品和一个烃源岩样品中的高分子量烷烃进行了定量测定,3个原油样品中nC40以上的高分子量烷烃的含量分布在22.8-38.0mg/g油。平均占nC22以上正构烷烃总量的16%。东10井烃源岩（2765.5m）中nC40以上的高分子量烷烃占nC22以上正构烷烃总量的32.9%,比同构造、同层位的东12井原油高出约1倍,这可能与运移效应有关。     

河南油田稠油油藏高温高压下储层敏感性试验研究

方法针对河南油田稠油油城回来水率低、提高排液量难度大等问题，选择有代表性的小层，开展了高温高压条件下的储层敏感性室内试验研究。目的保护油气层，充分发挥其潜力。结果提出了注蒸汽吞吐各生产环节中相应的油层保护措施，改善了稠油注蒸汽的吞吐效果。结论通过对80取口井32块样品的速敏性、水敏性、碱敏性以及正反向流动试验，取得了对储层粘土敏感性的定量认识。认为注入、采出液强度应限制在速敏极限值以下；周期采油过程中，高温期排液量不宜过大，中低温期配合降粘助排措施，适时强化排液；严格标定入井液的pH值，禁止不合格的化学剂溶液入井，避免碱敏作用对储层的伤害。     

高温高压CO_2驱油微观机理实验研究

运用自主开发研制的高温高压微观实验系统模拟了不同温度、不同压力下,CO2在储层孔隙体、喉道中的变化状态.利用微观实验可视化、真实化的特点,对CO2与原油相互作用的机理进行了研究.解释了CO2驱油效率高于其他驱油剂的主要原因.为油田矿场CO2驱油技术的现场实施提供了理论依据.     

静安堡油田高凝油油藏原油特性及影响因素的实验研究

方法利用室内实验．分析了静安堡油田高凝油特性及影响因素。目的通过高凝油特性及影响因素分析，为确定不同类型高凝油油藏合理开发，提供了可靠的依据。结果高凝油的析蜡温度主要受原油中蜡的最高碳原子数、含蜡量及压力的影响；凝固点主要受原油中含蜡量、组分的影响。结论开发高凝油油藏，地层温度应保持在原油析蜡温度以上，地层压力应保持在饱和压力附近。