易燃液体闪点预测模型综述

闪点(FP)是易燃液体及其分类标准的重要划分依据,同时也是衡量可燃液体火灾危险性的重要参数。闪点的确定将影响危险化学品的分类、储存、运输、使用、防火及危险品公示等各方面。为弥补实验测定的不足,借助模型预测来计算闪点具有重要的理论意义和实用价值。本文综述了易燃液体闪点的估算方法,主要分为三类:经验关联计算,基团贡献法计算和基于分子结构的模型预测,并讨论了三类方法各自的优势和不足。经验关联计算形式上简单,并且易于从实验数据中构建,一般与沸点相关联,使用数学回归或人工神经网络(ANN)方法获得。基团贡献法(GCM)是假设分子的性质是构成分子的所有基团贡献的函数,通过分子官能团贡献对闪点建立线性或非线性模型。基于分子结构的定量结构-性质关系,(QSPR)模型的建立与精度关键在于分子描述符的计算与筛选、模型建立的不同方法。近年来,鉴于各模型的优势与不足,将QSPR与其他预测模型和先进技术结合起来研究闪点与分子结构的相关性,是闪点预测的研究方向和热点,也为易燃液体混合物闪点的预测模型打下基础。     

低闪点原油作为主电站燃料的解决方案研究

对于一些缺少伴生气的油田,利用自产原油作为原油发电机燃料是最经济的选择。但自产原油闪点较低,将对电机安全运行形成安全隐患。本文介绍了第三方对原油电站使用燃料油闪点的要求,提出了使用负压闪蒸分离法提高原油闪点的解决方案,并进行了初步实验。实验结果表明,此方法可以提高原油闪点,与其他方法相比,本方案工艺流程简单,所需设备少,占地面积小,具有广阔的应用前景。     

海洋平台原油闪点提升技术分析研究

南海轻质油田原油闪点较低,低闪点原油直接用作燃料影响主电站正常运行和安全,不满足第三方的要求。常压蒸馏和负压闪蒸技术是两种提升原油闪点的可行方案,本文以南海EP和HZ油田为例,对两种处理流程以及操作参数进行模拟计算。详细对比分析常压蒸馏和负压闪蒸两种方案,并结合海上石油生产设施的实际情况,提出适用于不同性质原油的闪点提升技术方案。为经济的开发南海低闪点油田,提供了充足的理论分析和技术支持。     

基于岩石热解法的原油密度、粘度预测模型研究与应用

录井资料向定量化方向发展的最终目的之一就是对储层进行产能预测,而原油性质的变化是决定储层原油是否具备工业产出能力的重要因素,故在生产实践中原油性质的准确判断对试油方式的优选具有重要的指导意义。选取岩石热解录井派生参数(总产率指数、油产率指数、残余烃指数、原油轻重组分指数),采用多元线性回归算法,对长庆区块已完成试油井进行原油密度、粘度的校正,拟合效果精度较高,线性回归系数r²达到0.81以上,实现了原油密度、粘度的定量化评价,对后期提高储层流体性质的识别与评价具有重要的作用。     

小型炼油装置在海上原油主电站低闪点燃料解决方案中的应用研究

以南海X-2海上油气田项目为例,分析了原油燃料对海洋平台主电站的影响因素,指出低闪点原油会影响主电站的正常运行和安全,同时是第三方的强制要求。提出利用小型炼油装置提高低闪点原油的闪点,并对提高原油闪点的处理流程以及操作参数进行模拟计算,得到了该生产系统的压力、温度、设备负荷以及尺寸等参数。对油田油样进行了初步实验,实验结果表明此处理流程可行。对设备的工业应用进行了调研。研究结论表明,利用小型炼油装置将原油提高到要求的闪点是可行的,而且相对于传统的外购原油方案,节省了大量的费用和能源消耗,在海上油气田开发中具有重要意义。     

应用人工神经网络方法预测柴油闪点研究

精制柴油的闪点是衡量柴油质量的重要指标,在目前的柴油加氢脱硫生产中,主要根据操作人员的经验来调整,会产生较大的人为误差。文中采用了3种神经网络方法,研究在反应器床层最高点温度、分馏塔塔压、分馏塔塔顶温度、分馏塔塔底温度、氢油比波动时,预测柴油的闪点。结果表明,采用PSO-BP神经网络方法预测的柴油闪点均方误差为5.76×10~(-4),优于其它预测方法。     

提高塔河原油所产沥青闪点的技术研究

针对塔河原油所产沥青闪点低的情况,对影响沥青闪点的因素进行分析,并提出提高沥青闪点的措施。通过分析塔河原油的性质,并与科威特原油性质进行比较,认为沥青质含量过高是导致减压渣油闪点低的主要原因。通过优化原料及工艺,采用塔河原油掺炼催化裂化油浆的强化蒸馏技术,可显著提高沥青产品的闪点。将所得减压渣油再与SBR母液调合,可生产高品质的道路石油沥青。     

地层原油密度预测方法在莱州湾凹陷X构造中的应用

针对莱州湾凹陷X构造新近系稠油原油密度大、地层取样难度大,原油性质横向、纵向变化大的特点,以储集岩地化热解数据与实测原油化验分析数据为基础,运用多元线性回归分析模型,建立了适用于研究区地层原油密度预测的方法。应用结果表明,地层原油密度预测结果与实测原油密度绝对误差均小于0.01 g/cm³,原油密度预测结果精准有效。该方法对稠油层取样、测试工艺优选以及产能的释放具有一定指导意义和较好推广应用前景。     

闪点计算在先进控制中的应用

闪点是石油产品的一项重要物性,也是控制产品质量的一项重要指标。先进控制中的模型要求现场实时数据(DCS系统中的温度、压力、流量等)提供控制的目标和手段,所以要建立闪点等控制目标的有效数学模型。用线性方程关联ASTM 10％的计算方法可得到较满意的结果。     

渤南油田群混合原油凝固点变化规律研究

渤南油田群某油田产黑油和凝析油,二者的凝固点差别较大,直接影响到海底管道管输温度的设计。通过实验研究了不同配比、不同处理温度下黑油和凝析油混合后凝固点的变化规律,并利用实验数据对目前常用的2种混合原油凝固点预测模型进行了评价。模型Ⅱ(b12算法④)适用于渤南油田群混合原油凝固点预测,该模型也适用于其他类似油品混合后凝固点的预测。     

在用润滑油闪点的近红外光谱快速测定方法研究

美国环境保护署持续跟踪了18个大型水力压裂项目,对这些项目是否对社区饮用水产生不良影响进行研究。环境保护署制定了详细的研究计划和目标分析,并持续征求来自石油和天然气工业及其他相关利益者的意见,希望在2014年发表相关研究     

在用润滑油闪点的近红外光谱快速测定方法研究

提出一种基于近红外光谱技术的在用润滑油闪点快速检测方法。通过比较样品光谱和混入燃油光谱之间的光谱差异进行波段筛选,利用人工神经网络方法(ANN)和偏最小二乘方法(PLS)进行建模并比较,最终确定针对3个特征波段建立的ANN在用润滑油闪点的数学模型性能较优,模型的R2和SEP分别达到0.918 3、3.06℃。实验结果表明,ANN方法作为一种处理非线性问题的化学计量学方法,能较好地实现对在用润滑油的闪点测定。利用近红外光谱分析技术对快速判断润滑油是否混入轻质油料,为及时准确找到设备故障所在提供依据具有重要的指导意义。     

Transportation technology with pour point depressant and wax deposition in a crude oil pipeline

The authors systematically studied transportation technology with pour point depressant and wax deposition in an industrial crude oil pipeline. Experiment results manifest that beneficiated oil acquires obvious modification effect and the reheating temperature of intermediate heat stations should be above 55°C to avoid effect deterioration. Heating schemes are made with lower heating temperature and wider output range. Moreover, an applicable wax deposition model is established to predict wax deposition distribution along the pipeline under various operating conditions. Wax deposition rate varies severely along the pipeline and it is necessary to consider its non-uniformity in production.     

Prediction of asphaltene precipitation in crude oil

Asphaltene are problematic substances for heavy-oil upgrading processes. Deposition of complex and heavy organic compounds, which exist in petroleum crude oil, can cause a lot of problems. In this work an Artificial Neural Networks (ANN) approach for estimation of asphaltene precipitation has been proposed. Among this training the back-propagation learning algorithm with different training methods were used. The most suitable algorithm with appropriate number of neurons in the hidden layer which provides the minimum error is found to be the Levenberg–Marquardt (LM) algorithm. ANN's results showed the best estimation performance for the prediction of the asphaltene precipitation. The required data were collected and after pre-treating was used for training of ANN. The performance of the best obtained network was checked by its generalization ability in predicting 1/3 of the unseen data. Excellent predictions with maximum Mean Square Error (MSE) of 0.2787 were observed. The results show ANN capability to predict the measured data. ANN model performance is also compared with the Flory–Huggins and the modified Flory–Huggins thermo dynamical models. The comparison confirms the superiority of the ANN model.     

Application of bayesian regularized artificial neural networks to predict pour point of crude oil treated by pour point depressant

The pour point of the crude oil treated with the pour point depressant (PPD) is easily affected by the shear history effect. Models for pour point of PPD-treatment crude oil affected by the shear history effect based on Bayesian regularized artificial neural network (BRANN) were established. The results showed that BRANN models not only had a good ability of fitting to the training data, but also had a good ability of predicting the testing data. By evaluating network performance with several statistical indicators, the three models have excellent performance, high accuracy, and strong generalization ability. The influence of each parameter on the pour point were also investigated through a sensitivity analysis, which shows that the entropy generation due to viscous flow is the most important parameter in predicting the pour point.     

Predicting asphaltene precipitation during titration of diluted crude oil with paraffin using artificial neural network (ANN)

Abstract

Asphaltene precipitation from crude oil in underground reservoirs and on ground facilities is one of the major problems in a large portion of oil production units around the world. Many scaling equations and intelligent predictive models using the artificial neural network (ANN) are proposed in the literature but none of them can be applied when crude oil is diluted with any types of paraffin. In this study, feed forward artificial neural network is used for prediction of the amount of asphaltene precipitated weight percent of diluted crude oil with paraffin based on titration tests data from published literature. Trial and error method is utilized to optimize the artificial neural network topology in order to enhance its strength of generalization. The results showed that there is good agreement between experimental and predicted values. This predictive model can be applied to estimate the amount of asphaltene precipitated weight percent when the crude oil is diluted with paraffin and to avoid experimental measurement that is time-consuming and requires expensive experimental apparatus as well as complicated interpretation procedure.     

Viscosity prediction in selected Iranian light oil reservoirs: Artificial neural network versus empirical correlations

Viscosity is a parameter that plays a pivotal role in reservoir fluid estimations. Several approaches have been presented in the literature (Beal, 1946; Khan et al, 1987; Beggs and Robinson, 1975; Kartoatmodjo and Schmidt, 1994; Vasquez and Beggs, 1980; Chew and Connally, 1959; Elsharkawy and Alikhan, 1999; Labedi, 1992) for predicting the viscosity of crude oil. However, the results obtained by these methods have significant errors when compared with the experimental data. In this study a robust artificial neural network (ANN) code was developed in the MATLAB software environment to predict the viscosity of Iranian crude oils. The results obtained by the ANN and the three well-established semi-empirical equations (Khan et al, 1987; Elsharkawy and Alikhan, 1999; Labedi, 1992) were compared with the experimental data. The prediction procedure was carried out at three different regimes: at, above and below the bubble-point pressure using the PVT data of 57 samples collected from central, southern and offshore oil fields of Iran. It is confirmed that in comparison with the models previously published in literature, the ANN model has a better accuracy and performance in predicting the viscosity of Iranian crudes.     

油井压裂效果预测方法研究

压裂措施效果和影响因素之间关系复杂,常规多元回归法又很难确定两者之间的定量关系,而利用人工神经网络可以解决此问题。在对已压裂井增油措施效果评价的基础上,建立了不同措施类型、不同工艺类型的样本库。样本库中考虑的主要因素为:全井射开有效厚度、压裂层地层系数、压前产液量、压前含水率、压裂层数、总加砂量。利用人工神经网络方法建立起压裂效果与这些影响因素的定量关系,建立压裂效果预测模型。矿场应用结果表明,该方法预测结果可靠性较高。     

基于BP神经网络的原油含水率数据处理

原油含水率是油田开发状况的重要参数指标。由于原油含水率的测量受到多种因素的影响,且与其影响因素具有复杂的非线性关系,测量精度很难取得令人满意的效果。采用BP神经网络对原油含水率的测量数据进行处理,建立了原油含水率预测模型,使原油含水率的测量精度得到了改善。