高压注烃类气体过程中沥青质初始沉淀压力试验研究

为预防注烃类气体提高采收率过程中产生沥青质沉淀,对沥青质初始沉淀压力进行了试验研究.在分析注烃类气体过程中沥青质沉淀机理的基础上,通过自主研发的固相沉积激光探测装置,采用透光强度法测定了原油样品在不同温度下高压注气过程中沥青质的初始沉淀压力,并确定了沥青质沉淀的深度.试验得出,原油沥青质初始沉淀压力随温度升高而下降,测得44,80和123 ℃温度下原油的沥青质初始沉淀压力分别为44.1,39.7和35.2 MPa;每注入物质的量分数为1%的烃类气体,试验油样的沥青质初始沉淀压力升高0.5~0.6 MPa;井筒温度压力曲线与沥青质沉淀相包络线相结合预测井筒中出现沥青质沉淀的深度在1 800 m左右,与现场情况吻合较好.研究表明,原油中沥青质初始沉淀压力与注气量之间呈线性关系,可为现场注气驱油预防和清除沥青质沉积物提供理论依据.      

原油沥青质絮凝初始点的测定——以渤海绥中36-1油田稠油为例

沥青质在原油中的稳定性主要取决于原油对沥青质的溶解能力。当溶解能力下降到某一临界值后 ,沥青质就会从原油中絮凝析出 ,该临界值即为沥青质絮凝初始点。采用透光率法、粘度法和显微镜法对常压下渤海绥中 3 6 1油田稠油沥青质的絮凝初始点进行了测定。结果表明 ,粘度法和显微镜法测定结果比较准确、合理 ,而透光率法因受原油色深和分光光度计光源条件的限制而使沥青质絮凝初始点滞后 ;在给定压力下 ,温度升高会使原油沥青质絮凝初始点提前     

肯基亚克油田盐下8010井沥青质初始沉积压力温度预测

原油温度、压力和组成的改变均会引起沥青质发生絮凝和沉积,造成储层伤害和井筒堵塞。建立一种沥青质沉积条件的预测模型,根据盐下油田8010．井的井筒流体组成、原油和C7,组分的相对分子质量、原油和各组分的密度、沸点等数据,可以方便快速地确定原油沥青质沉积初始压力和温度。结果表明,肯基亚克8010井原油的沉积初始温度为70％,沉积初始压力为38．6MPa,比地层温度下的原油饱和压力高出6．6MPa。相应地可以推测出在1300-1400m井筒处开始发生沥青质沉积,沉积过程可能会持续几百米甚至直到地面泵阀处。实际发生的情况证实了这一预测的可靠性,为该油田制定预防和解除油井发生沥青质沉积的具体措施提供了理论依据。     

原油沥青质絮凝初始点的测定--以渤海绥中36-1油田稠油为例

沥青质在原油中的稳定性主要取决于原油对沥青质的溶解能力。当溶解能力下降到某一临界值后，沥青质就会从原油中絮凝析出，该临界值即为沥青质絮凝初始点。采用透光率法、粘度法和显微镜法对常压下渤海绥中36—1油田稠油沥青质的絮凝初始点进行了测定。结果表明，粘度法和显微镜法测定结果比较准确、合理，而透光率法因受原油色深和分光光度计光源条件的限制而使沥青质絮凝初始点滞后；在给定压力下，温度升高会使原油沥青质素凝初始点提前。     

CO_2注入过程中沥青质沉淀预测

注入CO2提高原油采收率过程中可能出现沥青质固相沉淀。鉴于沉淀沥青质的强极性,采用Anderko建立的缔合混合物状态方程描述沥青质的相行为,并由此推导沉淀沥青质组分的逸度计算公式,建立注气过程中气-液-沥青质三相相平衡数值计算模型。以某油田实际原油为例,利用模型计算了CO2注入过程中沥青质沉淀量,结果与实验数据相近,表明沥青质沉淀预测模型具有一定的准确性。在此基础上,预测了注气过程中沥青质沉淀变化规律:注入压力一定的情况下,沥青质沉淀量随着注入CO2量增加呈现先增加后减小的趋势,当CO2-原油体系中出现气相时,沥青质沉淀量达到最大;当CO2-原油体系中CO2物质的量分数一定时,在泡点压力附近沥青质沉淀量达到最大。图3表1参10     

沥青质沉淀点的测定与模型化计算

通过自行设计、组装了一套采用透光率法测定原油体系沥青质沉淀点的实验装置,并采用该仪器测定了6个浓度的沥青质+甲苯体系及4个浓度的原油+甲苯体系的正戊烷和正己烷沥青质沉淀点。模型计算中采用目前具有代表性的Hirschberg沥青质沉淀溶解度模型以拟合实验数据。结果发现由实验数据回归得到的原油溶解度参数明显高于状态方程法的计算结果;原始条件下沥青质的溶解度参数值明显低于被提纯后沥青质的溶解度参数值。     

伊朗碳酸盐岩油层注二氧化碳气导致沥青质沉淀引起的地层伤害

尽管二氧化碳（CO2）注入法是最广泛用于许多油层提高原油采收率的方法,但是这种方法会改变油藏流体的特性,进而带来诸如沥青质沉淀这样的麻烦。沥青质沉淀会堵塞住井筒和生产设备,从而降低注入能力和生产能力。本文介绍了伊朗西南部Kupal油田Bangestan油层CO2混相驱对沥青质沉淀的影响。用PVT（压力、容积、温度）测试法测量了CO2气与原油的混合物中沥青质沉淀的数量;进而通过高压岩心驱替实验弄清了油层条件下沥青质析出的机理。最后将实验结果与现有沥青质沉淀模型得到的结果进行了对比。     

肯基亚克油田盐下油藏8001井有机-无机复合堵塞的机理研究

通过对堵塞物的分离和组分分析,探讨了肯基亚克油田8001井井筒中有机一无机复合堵塞的机理。利用有机溶剂对其堵塞物进行了分离,各组分的含量如下：无机杂质5．31％,正庚烷沥青质48．23％,胶质10．87％。利用X射线衍射分析技术分析了无机物杂质的组成,其中的石英成分高达60．8％。结果表明,盐下油藏8001井井筒的堵塞主要是胶质、沥青质的沉积,并与无机杂质协同作用,加剧了堵塞物的形成;堵塞物中的主要无机杂质并不是来自于储层,而是由于各种施工措施带入的。通过沥青质沉积初始压力预测模型的建立,预测了8001井原油发生沥青沉积的初始压力和初始温度。利用核磁共振技术分析了8001井堵塞物中沥青质、8001井原油中沥青质以及地层沥青质的碳、氢元素组成。结果表明,堵塞物沥青质中的碳元素含量高于原油沥青质中的碳元素含量,堵塞物沥青质中的氢元素含量低于原油沥青质中的氢元素含量,而地层沥青质的碳元素含量最高、氢含量最低,意味着8001井堵塞物中的沥青质并非完全来自于原油,有部分沥青质可能来自于地层沥青层。     

井筒沥青质沉积位置预测方法

为了预防和控制井筒沥青质沉积，基于垂直井筒实际井身结构，考虑了油管、环空、套管、水泥环稳定传热以及地层段非稳定传热对井筒总传热系数的影响，结合能量守恒方程、热力学基本方程建立了井筒油气水三相流动压力模型、温度梯度模型。同时利用SRK Peneloux方程预测沥青质沉淀趋势，以塔里木盆地哈XX井为例分别计算了生产井的温度剖面、压力剖面以及沥青质沉积位置，并分析了油压、产油量、含水率以及井口温度对井筒沥青质沉积位置的影响。研究结果表明：新模型计算的温度、压力平均相对误差分别为1.14%和1.68%；井筒中沥青质沉积最厚位置随着油压、产油量的增加、含水率以及井口温度的降低而向井口移动；对比发现，油压对井筒沥青质沉积位置影响最大，因此建议通过调整井口油压来预防沥青质沉积。研究成果对预防井筒沥青质沉积具有指导意义。     

巴什托油田胶质沥青质堵塞机理及预测

巴什托油田生产过程中,"堵塞"现象一直伴随着生产存在,严重影响生产及油气开发.分析认为原油内部胶体不稳定,地层及井筒压降是导致沥青质沉淀堵塞井筒的主要原因.通过压力推导预测沥青质析出深度基本分布在500~2 400 m之间,在生产过程中,对该井段着重进行机械除垢,从而达到预防井筒堵塞的目的.     

Modeling Asphaltene Precipitation Under Gas Injection and Pressure Depletion Conditions

Asphaltene precipitation is a major problem during primary oil production and enhanced oil recovery in the petroleum industry. In this work, a series of experiments was carried to determine the asphaltene precipitation of bottom hole live oil during gas injection and pressure depletion condition with Iranian bottom hole live oil sample, which is close to reservoir conditions using high pressure-high temperature equilibrium cell. In the majority of previous works, the mixture of recombined oil (mixture dead oil and associated gas) was used which is far from reservoir conditions. The used pressure ranges in this work covers wide ranges from 3 to 35 MPa for natural depletion processes and 24–45 MPa for gas injection processes. Also, a new approach based on the artificial neural network (ANN) method has been developed to account the asphaltene precipitation under pressure depletion/gas injection conditions and the proposed model was verified using experimental data reported in the literature and in this work. A three-layer feed-forward ANN by using the Levenberg-Marquardt back-propagation optimization algorithm for network training has been used in proposed artificial neural network model. The maximum mean square error of 0.001191 has been found. In order to compare the performance of the proposed model based on artificial neural network method, the asphaltene precipitation experimental data under pressure depletion/gas injection conditions were correlated using Solid and Flory-Huggins models. The results show that the proposed model based on artificial neural network method predicts more accurately the asphaltene precipitation experimental data in comparison to other models with deviation of less than 5%. Also, the number of parameters required for the ANN model is less than the studied thermodynamic models. It should be noted that the Flory and solid models can correlate accurately the asphaltene precipitation during methane injection in comparison with CO₂ injection.     

The Effect of Temperature and Pressure on the Reversibility of Asphaltene Precipitation

A lot of hindrances are seen in petroleum operation, production, and transportation as a results of factors that related to asphaltene precipitation. It has great importance to investigate the reversibility of asphaltene precipitation under changes of effective factors on thermodynamic conditions such as pressure, temperature, and composition. In the present work the reversibility of asphaltene precipitation under changes of pressure and temperature was investigated for two kind of Iranian heavy oil. The stability test shows these samples are located at unstable region in aspect of asphaltene precipitation. The experimental procedure includes two parts, (a) decreasing pressure from initial reservoir pressure to near saturation pressure and surveying asphaltene content hysteresis with redissolution process at reservoir temperature, and (b) investigation of precipitated asphaltene in both precipitation and redissolution processes at different temperature and reservoir pressure. At each step IP143 standard test was used to measure precipitated asphaltene. It was concluded that above bubble point pressure, asphaltene precipitation is nearly reversible with respect to pressure for both samples and it was partially reversible with respect to the temperature for sample A, and accordingly pressurizing is acceptable method for solving the problem in both heavy asphaltenic crude oil samples and increasing temperature is acceptable method for solving asphaltene problem in crude oil sample A. Also density measurement of flashed oil confirmed that there is a little hysteresis in asphaltene content during redissolution and precipitation processes.     

Prediction of asphaltene precipitation by the extended Flory–Huggins model using the modified Esmaeilzadeh–Roshanfekr equation of state

Asphaltene precipitation is caused by a number of factors, such as the variation of pressure and temperature, the change in composition and the mixing of oil with diluting solvents. The deposition of asphaltene precipitation is one of the main problems with the oil industries, appearing in the well bore, the well tubing and the refining processes. This causes an increase in the operating costs and imposes the costs of cleaning and washing well tubing as well. Therefore, it would be economically beneficial to know under what conditions and to what amount the asphaltene precipitates.In this paper, a model is presented based on the Flory–Huggins theory of polymeric solutions. Because the interaction parameter term plays a key role in the asphaltene precipitation, a correlation is proposed to account for the effect of the solvent ratio in addition to molecular weight. Several adjustable parameters in terms of the interaction parameter are determined in this work using a series of experimental precipitation data from a crude oil sample of a field located in the southwest of Iran (oil sample 1), and applying a robust optimization method (the differential evolution). Regarding the influence of the solubility parameter on the accuracy of the final results, a comparison is made between the m-ER, PR and the SRK EOSs. Finally, the obtained results from the comparison between the asphaltene precipitation amounts of various solvents and the existing experimental values for another group of data from oil sample 1, and two other oil samples verify the accuracy of the presented model.     

A comprehensive experimental evaluation of asphaltene dispersants for injection under reservoir conditions

As the efficiency of dispersants with different origins is questionable for each typical oil sample, the present study provides a reproducible and reliable method for screening asphaltene dispersants for a typical asphaltenic crude oil. Four different asphaltene dispersants (polyisobutylene succinimide, polyisobutylene succinic ester, nonylphenol-formaldehyde resin modified by polyamines, and rapeseed oil amide) were prepared and their performance on two oils from an Iranian field under laboratory and reservoir conditions was studied. A thorough analysis including ash content and SARA tests was performed on the solid asphaltene particles to characterize the nature of deposits. Then a highly efficient carrier fluid, which is crucial when injecting dispersant into the wells, was selected from a variety of chemicals by comparing their solubility. In the next step, using an optical microscope, a viscometer, and a Turbiscan, the screening of dispersants under laboratory conditions was done on a mixture of dead oil and dispersant to evaluate the onset of asphaltene precipitation and its stability when titrating by a precipitant. Finally, two different mixtures of the efficient dispersants, live oil, and carrier fluid were used with the solid detection system (SDS) and the filtration method to examine their effects on the onset pressure of asphaltene precipitation and the asphaltene content of the crude oil under reservoir conditions. The results show that the combination of experimental methods used in this work could be consistently applied to screening asphaltene dispersants. Among the four different dispersants applied here, the dispersant based on nonylphenol-formaldehyde resin modified by polyamines showed the best performance on the available live oils. This chemical modified the onset pressure of asphaltene precipitation of light oil from 4300 psi to about 3600 psi and decreased the precipitated asphaltene of heavy oil by about 30 %.     

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.     

Study of asphaltene deposition from Tahe crude oil

Borehole blockage caused by asphaltene deposition is a problem in crude oil production in the Tahe Oilfield, Xinjiang, China. This study has investigated the influences of crude oil compositions, temperature and pressure on asphaltene deposition. The asphaltene deposition trend of crude oil was studied by saturates, aromatics, resins and asphaltenes (SARA) method, and the turbidity method was applied for the first time to determine the onset of asphaltene flocculation. The results showed that the asphaltene deposition trend of crude oil by the turbidity method was in accordance with that by the SARA method. The asphaltene solubility in crude oil decreased with decreasing temperature and the amount of asphaltene deposits of T739 crude oil (from well T739, Tahe Oilfield) had a maximum value at 60 o C. From the PVT results, the bubble point pressure of TH10403CX crude oil (from well TH10403CX, Tahe Oilfield) at different temperatures can be obtained and the depth at which the maximum asphaltene flocculation would occur in boreholes can be calculated. The crude oil PVT results showed that at 50 , 90 and 130 o C, the bubble point pressure of TH10403CX crude oil was 25.2, 26.4 and 27.0 MPa, respectively. The depth of injecting asphaltene deposition inhibitors for TH10403CX was determined to be 2,700 m.     

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.     

Developing a Scaling Equation as a Function of Pressure and Temperature to Determine the Amount of Asphaltene Precipitation

A simple and applicable scaling equation as a function of pressure, temperature, molecular weight, dilution ratio (solvent), and weight percent of precipitated asphaltene has been developed. This equation can be used to determine the weight percent of precipitated asphaltene in the presence of difference precipitants (solvents) and the amount of solvent at onset point. Since increasing the pressure of crude oil decreases the amount of asphaltene precipitation, the effect of reservoir pressure has been taken into account in developing this equation. The results obtained by using this equation are substantially different and more accurate from other developed scaling equations for asphaltene precipitation. By considering the effect of reservoir pressure in developing the scaling equation and application of a genetic algorithm, the unknown parameters of the scaling equation are simultaneously and without any reservation obtained. The most important application of this unique equation is in the determination of critical point of asphaltene precipitation, known as onset point, and asphaltene precipitation in gas injection operations for enhanced oil recovery. The results predicted using the scaling equations are compared with the authors' experimental and literature precipitation data and it is shown that they are in good agreement with our experimental data. The scaling equation can be used in the design of gas-injected reservoir to prevent precipitation of the asphaltene aggregates in the reservoir.     

注天然气油井沥青质析出规律研究

目的 解决东河区块原油在注气开采过程中沥青质沉积堵塞井筒问题。方法 采用高温高压固相沉积规律测试装置,基于光散射理论,研究了温度、压力、气油比等因素对沥青质析出特征的影响。结果 温度升高会增加沥青质在原油中的溶解度,促进原油稳定;等温降压过程中,沥青质随着压力降低逐渐析出,在泡点压力附近达到最大析出量,发生沥青质沉积堵塞油井的风险最大。DH-1井泡点压力对应井深2 140 m,与油井生产实际遇阻位置1 969 m接近,泡点压力可初步用于预测油井堵塞位置;溶解注气量越大,沥青质初始析出压力越大,沥青质析出压力区间也增大,沥青质沉积位置向油井深度下移。结论 研究揭示了注气过程沥青质的析出规律,对注天然气油井沥青质析出防治具有重要指导作用。