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Integrated reservoir/wellbore production model for oil field asphaltene deposition management
Journal of Petroleum Science and Engineering ( IF 5.168 ) Pub Date : 2020-04-01 , DOI: 10.1016/j.petrol.2020.107213
Mohammad Ghasemi , Eissa Al-Safran

Asphaltene deposition prevention, mitigation, and management remains a major challenge to the oil industry due to its complexity, poor understanding, and inadequate predictive tools. A literature review study on asphaltene deposition revealed a lack of integrative models that link reservoir, wellbore, and surface facility to predict asphaltene deposition taking into account the effect of their interaction on asphaltene deposition. In addition, most of the existing studies are focused on either modeling the thermodynamics aspects of asphaltene precipitation, or single-phase asphaltene deposition. Therefore, it is critical to model asphaltene deposition under multiphase flow conditions to, accurately, develop prevention, mitigation, and management strategies, which depends on not only asphaltene thermodynamics, but also multiphase flow hydrodynamics and behavior. The objective of this study is to develop a robust systematic approach for predicting asphaltene deposition in production system through coupling reservoir and wellbore production models, which provides a cost-effective optimal mitigation and management strategies. The proposed work in this study integrates five models, namely reservoir asphaltene deposition model, equation-of-state (EOS) model, asphaltene thermodynamics precipitation model, mechanistic multiphase flow model, and asphaltene deposition transport model. The above-mentioned models are integrated using developed workflow platform, which enables compositional tracking throughout the entire production system. Furthermore, experimental fluid characterization data was used to tune the EOS model to ensure accurate phase behavior and volumetric calculations, and to tune the thermodynamic asphaltene precipitation model. A field case input data is used to evaluate the proposed integrated model, which indicates severe asphaltene depositions in production tubing. The proposed model predicted the location and growth of asphaltene deposition thickness with time and space in the inner production-tubing wall. The model results also show that local asphaltene deposition reduced tubing cross-sectional area, increasing in-situ superficial oil and gas velocities, thus increasing pressure drop and decreasing flowrate. Sensitivity analyses to investigate several parameters such as depletion drive mechanism, asphaltene particle size, and injection of CO2 rich gas on asphaltene deposition show excellent results that are aligned with the physical and theoretical understanding of asphaltene deposition. These results are critical in selecting, optimizing, and implementing asphaltene deposition mitigation and management strategies, which affects project economics and safety.



中文翻译:

用于油田沥青质沉积管理的集成油藏/井筒生产模型

沥青质沉积的预防,缓解和管理由于其复杂性,了解不足和缺乏预测工具而仍然是石油工业的主要挑战。关于沥青质沉积的文献综述研究表明,缺乏综合模型将储层,井眼和地表设施联系起来,以考虑沥青相互作用对沥青质沉积的影响来预测沥青质沉积。此外,现有的大多数研究都集中在对沥青质沉淀的热力学方面进行建模或单相沥青质沉积方面。因此,对多相流条件下的沥青质沉积进行建模以准确地制定预防,缓解和管理策略至关重要,这不仅取决于沥青质的热力学,而且还有多相流的流体动力学和行为。这项研究的目的是开发一种可靠的系统方法,通过耦合储层和井眼生产模型来预测生产系统中的沥青质沉积,从而提供一种具有成本效益的最优减缓措施和管理策略。本研究中拟议的工作整合了五个模型,即储层沥青质沉积模型,状态方程(EOS)模型,沥青质热力学降水模型,机理多相流模型和沥青质沉积运移模型。上面提到的模型是使用开发的工作流程平台集成的,该平台可以在整个生产系统中进行成分跟踪。此外,实验流体特征数据用于调整EOS模型,以确保准确的相行为和体积计算,以及调整热力学沥青质沉淀模型。现场案例输入数据用于评估建议的集成模型,该模型表明生产油管中沥青质沉积严重。提出的模型预测了内部生产管壁中沥青质沉积厚度随时间和空间的位置和增长。模型结果还表明,局部沥青质沉积减少了油管的横截面积,增加了原地表层油气的流速,从而增加了压降并降低了流量。进行敏感性分析以研究多个参数,例如耗尽驱动机理,沥青质粒径和CO注入 并调整了热力学沥青质的降水模型。现场案例输入数据用于评估建议的集成模型,该模型表明生产油管中沥青质沉积严重。提出的模型预测了内部生产管壁中沥青质沉积厚度随时间和空间的位置和增长。模型结果还表明,局部沥青质沉积减少了油管的横截面积,增加了原位表观油气速度,从而增加了压降并降低了流量。进行敏感性分析以研究多个参数,例如耗尽驱动机理,沥青质粒径和CO注入 并调整热力学沥青质的降水模型。现场案例输入数据用于评估建议的集成模型,该模型表明生产油管中沥青质沉积严重。提出的模型预测了内部生产管壁中沥青质沉积厚度随时间和空间的位置和增长。模型结果还表明,局部沥青质沉积减少了油管的横截面积,增加了原地表层油气的流速,从而增加了压降并降低了流量。进行敏感性分析以研究多个参数,例如耗尽驱动机理,沥青质粒径和CO注入 这表明生产油管中沥青质沉积严重。提出的模型预测了内部生产管壁中沥青质沉积厚度随时间和空间的位置和增长。模型结果还表明,局部沥青质沉积减少了油管的横截面积,增加了原地表层油气的流速,从而增加了压降并降低了流量。进行敏感性分析以研究多个参数,例如耗尽驱动机理,沥青质粒径和CO注入 这表明生产油管中沥青质沉积严重。提出的模型预测了内部生产管壁中沥青质沉积厚度随时间和空间的位置和增长。模型结果还表明,局部沥青质沉积减少了油管的横截面积,增加了原位表观油气速度,从而增加了压降并降低了流量。进行敏感性分析以研究多个参数,例如耗尽驱动机理,沥青质粒径和CO注入 增加地表浅层油气速度,从而增加压降并降低流量。进行敏感性分析以研究多个参数,例如耗尽驱动机理,沥青质粒径和CO注入 增加地表浅层油气速度,从而增加压降并降低流量。进行敏感性分析以研究多个参数,例如耗尽驱动机理,沥青质粒径和CO注入2种富含气体的沥青质沉积显示出优异的结果,这与对沥青质沉积的物理和理论理解是一致的。这些结果对于选择,优化和实施沥青质沉积物缓解和管理策略至关重要,这会影响项目的经济性和安全性。

更新日期:2020-04-01
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