Arbuckle地层注CO2地震活动风险的概率评价

K. Ochie, Moghanloo Rouzbeh, J. Daneshfar, J. Burghardt
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引用次数: 0

摘要

本文研究了贝叶斯定理的应用,以评估与美国中部大陆南部的Arbuckle地层二氧化碳封存相关的诱发地震活动风险。地质封存可以有效地减少二氧化碳排放,否则就会释放到大气中,实现2021年联合国气候变化大会(COP26)承诺的气候目标,然而,对二氧化碳注入相关风险的担忧,以及执行碳捕集利用和封存项目所需基础设施的经济挑战,阻碍了充分发挥其巨大潜力。主要目标通常是在地质时间内储存二氧化碳;因此,地质力学风险,如现场的地震活动或潜在的二氧化碳通过密封泄漏,不能忽视,被认为是决定项目成功的要求之一。本文详细阐述了可能影响工程寿命和成功的潜在地震事件的风险。准确的风险评估是环境、经济和安全问题的关键,也是获得美国环境保护署VI类许可的要求之一。我们使用了贝叶斯方法,这是一种统计模型,其中随机概率分布用于表示模型内的不确定性,包括输入/输出参数。以俄克拉何马州为例,我们利用基于系统物理模型的数据和过去观察/监测的故障细节来评估该地区未来的潜在风险。在我们的方法中,我们为正在调查的区域建立当前应力状态的概率,然后监测应力状态如何演变。计算应力状态概率分布,以评估在一定孔隙压力范围内激活临界断层的概率。结果表明,我们可以估计地层中诱发地震活动的概率。根据我们的建模结果,在初始注入压力下,在Arbuckle地层中引入地震活动的风险为30%。基于这些结果,我们进一步进行敏感性分析,以确定多重预测因子依赖于风险水平的特征。在分析的大多数情况下,由于应力状态约束很差,注入诱发地震活动性的风险仍大于30%。在堪萨斯州的Arbuckle地层引入应力状态约束后,地震活动的风险降低到10%。考虑到我们的工作结果,作业者可以优化现场筛选并收集额外的数据,以限制地质力学风险评估中固有的不确定性,并在作业过程中做出明智的决策。这项工作的结果表明,在Arbuckle地层中以较低的速率进行二氧化碳的地质储存是一种可行的安全策略,可以在选定的地区实现气候目标,并且在这些地区获得应力数据具有信息价值。
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A Probability Evaluation of Seismicity Risks Associated with CO2 Injection into Arbuckle Formation
This paper examines the application of Bayes’ theorem to evaluate risk of induced seismicity associated with CO2 sequestration in the Arbuckle Formation, which extends across the southern Mid-Continent of the US. Geological storage can effectively contribute to reducing emission of CO2, otherwise released into the atmosphere, achieving the climate goals committed in the 2021 United Nations Climate Change Conference (COP26), however, concerns about risks associated with CO2 injection along with economic challenges of infrastructure required to execute the Carbon Capture Utilization and Storage projects stand against full realization of remarkable potentials. The main goal is usually for CO2 to be stored over geologic time; hence, geomechanical risks such as the seismicity in the field or potential CO2 leakage through seals cannot be ignored and is considered as one of the requirements to determine success of the project. This paper elaborates the risk of potential seismic events that can impact the longevity and success of projects. Accurate risk estimation is key for environmental, economic, and safety concerns and is also one of the requirements to get class VI permits from the US Environmental Protection Agency. We utilized the Bayesian approach, a statistical model where a random probability distribution is used to represent uncertainties within the model, including both input/output parameters. Using Oklahoma as a case study we utilized data from established physics-based models of the system and the details from past observed/monitored failures to evaluate future risk potential for the area. In our approach, we establish the current probability for the state of stress for the area under investigation, then monitor how the state of stress evolves. The stress state probability distribution is calculated to evaluate the probability of activating a critically oriented fault over a range of specified pore pressures. The results suggest that we can estimate the probability of inducing seismicity in the formation. Based on our modelling results, at initial injection pressuresthere is a 30% risk of introducing seismicity in the Arbuckle formation. Based on these results, we went further to conduct a sensitivity analysis to determine the features with multiple predictor dependence on the risk level. In most cases analyzed the risk of induced seismicity by injection is still greater than 30% due to the stress state being very poorly constrained. Introducing stress state constraints from the Arbuckle formation in Kansas State, the risk of seismicity reduced to 10%. Considering the results from our work, operators can optimize the site screening and collect additional data to constrain inherent uncertainties in geomechanical risk evaluation and make informed decisions during operations. The result from this work shows that geological storage of CO2 at reduced rates in the Arbuckle formation can be a feasible safe strategy towards achieving climate goals in selected areas and there is value of information in obtaining stress data in these areas.
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