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Characterization of CO_2 storage and enhanced oil recovery in residual oil zones

机译:表征CO_2的储存并提高剩余油区的采油率

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摘要

Residual oil zones (ROZs) are reservoirs in which oil is swept over geologic time period and exists at residual saturation. The oil in such reservoirs cannot be commercially exploited using conventional oil recovery methods as the oil exists at residual oil saturation. Instead, enhanced oil recovery methods such as CO2 injection are required. Recently, ROZs have been increasingly studied as potential CO2 storage targets. In spite of increased interest in ROZs, there are significant gaps in the knowledge of parameters and processes that impact CO2 storage and oil recovery. In this work, we identify key geologic and operational characteristics that affect CO2 storage capacity and oil recovery potential by performing Monte Carlo simulations and sensitivity analysis. In addition to CO2 storage capacity, we also characterize the long-term CO2 fate in ROZs. The distinction of CO2 storage in ROZs from conventional oil reservoirs and saline aquifers are also characterized. Furthermore, predictive models based on machine learning techniques are developed to estimate CO2 storage and oil production potentials for ROZs. The applicability of the predictive models is demonstrated for five ROZs in the Permian Basin. Published by Elsevier Ltd.
机译:剩余油层(ROZs)是在整个地质时期内驱油的储层,并以剩余饱和度存在。这种油藏中的油不能使用常规的采油方法进行商业开发,因为该油以残余油饱和状态存在。相反,需要增强的采油方法,例如注入二氧化碳。最近,人们越来越多地研究了ROZ作为潜在的CO2储存目标。尽管对ROZ的兴趣日益增加,但是影响CO2储存和采油的参数和过程的知识仍然存在很大差距。在这项工作中,我们通过进行蒙特卡洛模拟和敏感性分析,确定影响CO2储存能力和石油采收潜力的关键地质和作业特征。除了CO2的存储能力,我们还描述了ROZ中CO2的长期命运。还描述了ROZ中的CO2储存与常规油藏和盐水层的区别。此外,还开发了基于机器学习技术的预测模型来估计ROZ的CO2储存和产油潜力。预测模型的适用性在二叠纪盆地的五个ROZ中得到了证明。由Elsevier Ltd.发布

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