V. Banjara , E. Pereyra , C. Avila , D. Murugavel , Sarica C
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For benchmarking, the performance of this proposed model has been compared with experimental data for lab-scale geometry and with the OLGA simulations for field-scale geometry and higher operating pressures. The results demonstrate moderate errors in predicting slug characteristics, affirming the model's reliability and applicability. Additionally, a severe slugging envelope prediction approach has been proposed utilizing the developed model. The proposed severe slugging envelopes modeling approach can demarcate the severe slugging flow region for a given pipeline riser geometry and predict severe slug characteristics, including slug length and slug time within that severe slug flow region. This work significantly contributes to flow assurance strategies in production, optimizing hydrocarbon extraction processes and minimizing operational disruptions.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"244 ","pages":"Article 213446"},"PeriodicalIF":0.0000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improved severe slugging modeling and mapping\",\"authors\":\"V. Banjara , E. Pereyra , C. Avila , D. Murugavel , Sarica C\",\"doi\":\"10.1016/j.geoen.2024.213446\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>One of the common flow assurance issues in offshore production facilities is riser-based severe slugging. This phenomenon can also occur in horizontal wells, hindering oil and gas production. Severe slugging is a cyclic process that occurs in the late life of reservoirs when there is not enough energy available from the reservoir to push the liquid out of the riser. It is highly undesirable as it results in large fluctuations in pressure, oil, and gas flow rates at the outlet of the riser. To address this challenge, we propose an improved one-dimensional severe slugging modelthat enhances the blowout step of the slugging cycleand Compared to the existing one-dimensional models. For benchmarking, the performance of this proposed model has been compared with experimental data for lab-scale geometry and with the OLGA simulations for field-scale geometry and higher operating pressures. The results demonstrate moderate errors in predicting slug characteristics, affirming the model's reliability and applicability. Additionally, a severe slugging envelope prediction approach has been proposed utilizing the developed model. The proposed severe slugging envelopes modeling approach can demarcate the severe slugging flow region for a given pipeline riser geometry and predict severe slug characteristics, including slug length and slug time within that severe slug flow region. This work significantly contributes to flow assurance strategies in production, optimizing hydrocarbon extraction processes and minimizing operational disruptions.</div></div>\",\"PeriodicalId\":100578,\"journal\":{\"name\":\"Geoenergy Science and Engineering\",\"volume\":\"244 \",\"pages\":\"Article 213446\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geoenergy Science and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2949891024008169\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"0\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoenergy Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949891024008169","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
摘要
海上生产设施中常见的流量保证问题之一是立管严重抽吸。这种现象也可能发生在水平井中,阻碍油气生产。严重堵塞是一种周期性过程,发生在储油层后期,当储油层没有足够的能量将液体挤出隔水管时就会出现。这种现象非常不可取,因为它会导致隔水管出口处的压力、石油和天然气流量出现大幅波动。为了应对这一挑战,我们提出了一种改进的一维严重抽吸模型,与现有的一维模型相比,该模型增强了抽吸循环中的井喷步骤。为了确定基准,我们将该模型的性能与实验室规模几何形状的实验数据以及现场规模几何形状和更高工作压力的 OLGA 模拟进行了比较。结果表明,在预测弹头特性时误差适中,从而肯定了模型的可靠性和适用性。此外,还利用所开发的模型提出了一种严重抽油包络预测方法。所提出的严重堵塞包络建模方法可以为给定的管道立管几何形状划分出严重堵塞流动区域,并预测严重堵塞特征,包括该严重堵塞流动区域内的堵塞长度和堵塞时间。这项工作对生产中的流量保证策略、优化碳氢化合物提取过程和最大限度地减少运行中断做出了重大贡献。
One of the common flow assurance issues in offshore production facilities is riser-based severe slugging. This phenomenon can also occur in horizontal wells, hindering oil and gas production. Severe slugging is a cyclic process that occurs in the late life of reservoirs when there is not enough energy available from the reservoir to push the liquid out of the riser. It is highly undesirable as it results in large fluctuations in pressure, oil, and gas flow rates at the outlet of the riser. To address this challenge, we propose an improved one-dimensional severe slugging modelthat enhances the blowout step of the slugging cycleand Compared to the existing one-dimensional models. For benchmarking, the performance of this proposed model has been compared with experimental data for lab-scale geometry and with the OLGA simulations for field-scale geometry and higher operating pressures. The results demonstrate moderate errors in predicting slug characteristics, affirming the model's reliability and applicability. Additionally, a severe slugging envelope prediction approach has been proposed utilizing the developed model. The proposed severe slugging envelopes modeling approach can demarcate the severe slugging flow region for a given pipeline riser geometry and predict severe slug characteristics, including slug length and slug time within that severe slug flow region. This work significantly contributes to flow assurance strategies in production, optimizing hydrocarbon extraction processes and minimizing operational disruptions.