Dead oil viscosity is one of the most unreliable properties to predict with classical black oil correlations. This results mostly from the large effect that oil type has on viscosity. Two dead oil samples with identical APIs (https://www.eia.gov/today), (https://en.wikipedia.org/a) and can have even an order of magnitude difference in viscosity at the same temperature (Dindoruk and Christman, 2004). In this work, we tried to limit this spread to a certain degree by incorporating a parameter such as MW to capture additional information for the character of the oil. Limitations of the classical black oil correlations became even more prominent when a wide spectrum of viscosity values coupled with a wide range of temperatures are considered. Given the constraints of limited input variables, the problem becomes particularly challenging for heavy-extra heavy oils with high asphaltene content (Sinha et al., 2019), where prediction errors could easily be as high as a couple of log-cycles. Even though there are several viscosity correlations available in the industry (Ali, 2003)- (Bergman and Sutton, 2009), (De Ghetto and Villa, 1994), (Dindoruk and Christman, 2004), (Mehrotra, 1992), (Motahhari et al., 2013), (Naseri et al., 2005), (Lindeloff et al., 2003), (Lohrenz et al., 1964), (Pedersen and Fredenslund, 1987) (Petrosky and Farshad, 1995), (Peng and Robinson, 1976), (Standing, 1977), (Teja and Rice, 1981), (Yarranton et al., 2013) most of those correlations are only applicable to the oil samples belonging to specific geographical regions and/or for structurally similar oils, because of the inherent bias in the training datasets used in the development of correlations. Therefore, they are predictively valid for a relatively narrower range of oils and/or viscosity. In this work, we considered a very wide range of oils (6° API to 50° API). Therefore, producing two easy to use viscosity correlations for API (https://www.eia.gov/today), (https://en.wikipedia.org/a) gravities above and below 20° API that can readily predict the viscosity at any desired temperature within an extended temperature range (15 °C to 160 °C). Also, the two sets of correlations were kept compatible in a region in the vicinity of switching points (20 API and 36 cp) so that they will have a proper transition from one branch to another. While the range of fluid properties is very wide, we were able to keep the input parameters to a minimum in terms of defining the character of the fluid (molecular weight and specific gravity). We demonstrated that the proposed correlation along with the given methodology performs much better than the leading correlations with the similar input proxies published in the literature for a wide range of viscosities (0.42 cp to 860, 000 cp). The use cases for the proposed correlation can be divided into three parts: 1) Prediction of the dead oil viscosity with limited input data, 2) use of limited in-hand viscosity or reference viscosity data to generate viscosities for the conditions that are hard to perform accurate experimentation or simply not having the physical sample in hand to do additional experiments(for example, for thermal recovery processes where viscosity is needed at elevated temperatures, or to construct the lift curves and/or tables for pipeline flow at lower temperatures)) and 3) it can be used to check the consistency and the quality of the existing data. In addition to classical correlation development efforts using known but limited physical control parameters, we have also attempted to model the viscosity with various machine learning methods K-Nearest Neighbor (KNN) (Cover and Hart, 1967) and Kernel-based Support Vector Machine (KSVM)) (https://docs.oracle.com/c, 2835), (Suykens and Vandewalle, 1999) and compared the outcome to each other and as well as against the proposed correlation. Based on calculated statistical parameters and cross-plots, the proposed correlation performed better than the other leading viscosity correlations (Bergman and Sutton, 2009), (Lindeloff et al., 2003), (Lohrenz et al., 1964), (Pedersen and Fredenslund, 1987), (Pedersen et al., 1984), (Petrosky and Farshad, 1995), (Standing, 1977), (Teja and Rice, 1981), (Yarranton et al., 2013) and as well as the selected supervised machine learning regression principles (https://en.wikipedia.org/b), (https://machinelearningma) such as KNN and KSVM. The subject correlation also helps in improving the accuracy as well as guiding the performance of these otherwise “Blackbox” machine learning principles as it can fill the gaps in the data especially in the context of extending the tuned viscosities based on a single point measurement (reference viscosity) in temperature domain. Furthermore, we also explain how it can be combined with Sinha et al. (Sinha et al., 2019) relative viscosity correlation to include the impact of asphaltene concentration to be able to estimate vertical or areal viscosity variations which can also ultimately help to improve the mobility cut-off predictions of the asphaltene/tar mat zones or heavier fluids.

死油粘度是用经典黑油相关性预测的最不可靠的特性之一。这主要是由于油类型对粘度的巨大影响。具有相同API（https://www.eia.gov/today）（https://en.wikipedia.org/a）的两个死油样品，并且在相同温度下粘度甚至可以有一个数量级的差异（ Dindoruk和Christman，2004年）。在这项工作中，我们试图通过合并诸如MW的参数以在某种程度上限制这种扩散，以捕获有关油特性的其他信息。当考虑广泛的粘度值和广泛的温度范围时，经典黑油相关性的局限性变得更加突出。鉴于有限输入变量的限制，对于具有高沥青质含量的重质超重油而言，这个问题变得尤为具有挑战性（Sinha等人，2019），因为预测误差很容易高达几个对数循环。即使行业中存在几种粘度相关性（Ali，2003）-（Bergman和Sutton，2009），（De Ghetto和Villa，1994），（Dindoruk和Christman，2004），（Mehrotra，1992），（Motahhari等人（2013），（Naseri等人，2005），（Lindeloff等人，2003），（Lohrenz等人，1964），（Pedersen和Fredenslund，1987）（Petrosky和Farshad，1995），（ Peng和Robinson，1976年），（Standing，1977年），（Teja和Rice，1981年），（Yarranton等人，2013年）大多数相关性仅适用于属于特定地理区域和/或结构上的油样。类似的油 因为相关性开发中使用的训练数据集存在固有偏差。因此，对于油和/或粘度相对较窄的范围，它们具有可预测的有效性。在这项工作中，我们考虑了范围很广的机油（API为6°至50°API）。因此，对于API（https://www.eia.gov/today）（https://en.wikipedia.org/a）产生两个易于使用的粘度相关性，可以轻松预测API高于和低于20°的重力。在扩展温度范围（15°C至160°C）内任何所需温度下的粘度。而且，两组相关性在切换点（20 API和36 cp）附近的区域中保持兼容，因此它们将从一个分支正确过渡到另一个分支。虽然流体性质的范围非常广泛，我们能够在定义流体特性（分子量和比重）方面将输入参数保持在最低水平。我们证明，对于广泛的粘度范围（0.42 cp至860，000 cp），与给定方法论相关的拟议相关性要比文献中发表的相似输入代理的领先相关性好得多。提出的相关性的用例可以分为三个部分：1）使用有限的输入数据来预测死油粘度，2）使用有限的手工粘度或参考粘度数据来生成难以满足条件的粘度进行准确的实验，或者只是手头没有物理样品做其他实验（例如，对于在高温下需要粘度的热采工艺，或为低温下的管道流量构建升力曲线和/或表格）和3），可用于检查现有数据的一致性和质量。除了使用已知但有限的物理控制参数进行经典相关性开发工作之外，我们还尝试使用各种机器学习方法（最近的K近邻（KNN）（Cover and Hart，1967））和基于内核的支持向量机（ （KSVM））（https://docs.oracle.com/c，2835）（Suykens和Vandewalle，1999）并比较了彼此的结果以及提出的相关性。根据计算出的统计参数和交叉图，建议的相关性比其他主要的粘度相关性更好（Bergman和Sutton，2009）（Lindeloff等，2003），（Lohrenz等，1964），（Pedersen和Fredenslund，1987），（Pedersen等。 ，1984），（Petrosky和Farshad，1995），（Standing，1977），（Teja和Rice，1981），（Yarranton等人，2013），以及选定的监督式机器学习回归原理（https：// en.wikipedia.org/b）（https：// machinelearningma），例如KNN和KSVM。主题相关性还有助于提高准确性并指导这些其他“黑匣子”机器学习原理的性能，因为它可以填补数据中的空白，尤其是在基于单点测量扩展调整的粘度的情况下（参考粘度）。此外，我们还将说明如何将其与Sinha等人结合使用。（Sinha et al。，2019）相对粘度相关性，包括沥青质浓度的影响，以便能够估算垂直或面积粘度变化，这最终还可以帮助改善对沥青质/沥青垫层区域或较重区域的迁移率截止预测液体。