Laboratory and Theoretical Determination of Asphaltene Precipitation/Deposition/Inhibition in Canadian Oil Reservoirs

沥青质是在一定的热力学条件和流体特性下可能沉淀的化学芳香族化合物。沥青质沉淀/沉积通过润湿性改变和堵塞机制影响石油生产和运输操作。在加拿大各地的许多油藏中发现了这种不期望的行为，导致显著的资本/运营成本和环境问题。进一步了解和更准确地预测储层岩石中的沥青质特征和沉淀/沉积过程对于更好地设计、实施和控制石油生产作业至关重要。 该研究计划旨在通过实验研究结合Lattice Boltzmann方法，计算流体动力学，状态方程（EOS），统计工具和智能技术的理论建模，更好地了解沥青质表征，沉淀和抑制机制。提出的研究旨在减少或消除现有的缺点（过拟合，不可行的再训练和陷入局部最优）与传统的方法，并提供更好的准确性和可靠性方面的热力学行为，沉淀沥青质的程度，和沥青质的性质。微流控芯片将使在微观尺度上研究多孔介质中的随机孔隙结构的流动成为可能。在表征阶段之后，将在多孔介质中进行流动测试，以跟踪沥青质沉淀、沉积或/和溶解的可能性。将采用气相色谱仪（GC）、粘度计和高效液相色谱仪（HPLC）进行沥青质表征和沉淀现象识别。此外，视觉和定量方法（近红外（NIR）和高压显微镜（HPM））将用于研究关键因素（压力和温度）对沥青质的起始和形态行为的影响。 利用实验和文献数据，更好的统计和连接模型（非线性回归方程）将开发相关的温度，压力和分子量的沥青质沉淀/沉积。本研究还将检查EOS模型的常数和可调参数，以更好地表征沥青质，研究其热力学行为，并预测沉淀/沉积的发生。在沥青质沉淀/沉积调查之后，将在静态和动态条件下检查用于防止/去除沥青质沉积的化学方法和超声波辐射。实验和模拟研究将用于寻找最佳的操作条件（温度和组成），在沥青质沉积和抑制操作，使用遗传算法的方法。

Asphaltenes are chemical aromatic compounds that may precipitate at certain thermodynamic conditions and fluid characteristics. Oil production and transportation operations can be affected by asphaltene precipitation/deposition through wettability alteration and blockage mechanisms. This undesirable behavior is found in many oil reservoirs across Canada, resulting in significant capital / operating costs and environmental issues. Further understanding and more accurate prediction of asphaltene characteristics and precipitation/deposition processes in reservoir rocks are vital for better design, implementation, and control of oil production operations. This research program aims to develop better understanding of asphaltene characterization, precipitation, and inhibition mechanisms through experimental studies combined with theoretical modeling by Lattice Boltzmann method, Computational Fluid Dynamics, Equation of State (EOS), statistical tools, and smart techniques. The proposed research aims to reduce or eliminate the existing drawbacks (overfitting, non-feasible retraining and being stuck in local optima) with conventional methods and offer better accuracy and reliability in terms of thermodynamic behavior, extent of precipitated asphaltene, and asphaltene properties. Microfluidic chips will enable studies of the flow in porous media of random pore structures at microscale. After a characterization stage, flow tests will be conducted in porous media to track the likelihood of asphaltene precipitation, deposition or/and dissolution. Gas Chromatograph (GC), viscometer, and High Performance Liquid Chromatography (HPLC) will be employed for the purposes of asphaltene characterization and precipitation phenomena identification. In addition, visual and quantitative methodologies (Near Infra Red (NIR) and High Pressure Microscopy (HPM)) will be used to study the impacts of key factors (pressure and temperature) on the onset and morphological behavior of asphaltene. Utilizing the experimental and literature data, better statistical and connectionist models (non-linear regressive equations) will be developed to relate temperature, pressure, and molecular weight to asphaltene precipitation/deposition. This research will also examine constants and adjustable parameters of the EOS model in order to better characterize asphaltenes, study their thermodynamic behavior, and predict precipitation/deposition occurrence. After the asphaltene precipitation/deposition investigations, chemical methods and ultrasonic irradiation for prevention/removal of asphaltene deposition will be examined under static and dynamic conditions. The experimental and modeling studies will be used to find optimal operating conditions (temperature and composition) in asphaltene deposition and inhibition operations using genetic algorithm method.