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轻质油油藏注空气自发点燃延迟时间预测模型 被引量:4

A predictive model of spontaneous ignition delay of light-oil air-injection
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摘要 轻质油油藏注空气是行之有效并且正在兴起的提高采收率技术,适用于注水后期油藏的三次采油,尤其适用于注水困难的低渗透油藏的二次采油。轻质油油藏注入空气后需要多长时间才能发生自发点燃(或引导至高效的氧化反应)是当前现场操作中最为关心的问题之一。根据轻质原油低温氧化动力学原理,推导出轻质油油藏注空气自发点燃延迟时间预测模型及其近似解析解。因为实验室内难以实现模拟油藏绝热条件下的自放热过程,所以只有通过现场试验验证模型的可靠性。利用胜利油区某低渗透区块注空气先导试验数据预测计算得到自发点燃延迟时间为12d,该区块注空气现场试验结果显示,注气后第11d注入井周围有自发点燃或高效氧化(放热)反应的迹象,验证了模型的指导作用。 Air-injection is an emerging IOR technology for recovery of light oils, which has proven to be a successful process for tertiary oil recovery after water flooding, and especially effective as a secondary oil recovery method for low permeability oil reservoirs where water injection is not feasible. Air injection is currently considered by many operators for application in their assets. To achieve an effective and complete oxygen consumption, auto or spontaneous ignition is one of the most concerned issues for the air injection process. A prediction model for the spontaneous ignition process during air injection into light oil reservoirs is developed in this study, which is based on kinetics of low temperature oxidation (LTO) of light-oil in a nearadiabatic process. An analytical solution of the model is given. The only way to validate the model is to implement pilot because it is too difficult to replicate the self-energy generation process under adiabatic conditions of the field by laboratory experiments. To validate the model, a case study is conducted based on a field pilot data in a low permeability unit of Shengli oilfield. The predicted spontaneous ignition delay is 12 days under the reservoir conditions, while there is evidence to indicate a high rate oxidation occurred in the reservoir after 11 days of air injection.
出处 《油气地质与采收率》 CAS CSCD 北大核心 2011年第3期61-63,66,共4页 Petroleum Geology and Recovery Efficiency
基金 中国石油化工股份有限公司先导性科技项目"低渗透油田注空气提高采收率技术研究"(P06041)
关键词 注空气 轻质油油藏 自发点燃 低温氧化 自发点燃延迟时间 预测模型 air-injection light-oil reservoir spontaneous ignition low temperature oxidation spontaneous ignition delay prediction model
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参考文献20

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