非常规油藏液体井混合多相速率预测模型

Utkarsh Sinha, Hardikkumar Zalavadia, P. Chauhan, S. Sankaran
{"title":"非常规油藏液体井混合多相速率预测模型","authors":"Utkarsh Sinha, Hardikkumar Zalavadia, P. Chauhan, S. Sankaran","doi":"10.2118/212981-ms","DOIUrl":null,"url":null,"abstract":"\n The development of shale plays requires accurate forecasting of production rates and expected ultimate recoveries, which is challenging due to the complexities associated with production from hydraulically fractured horizontal wells in unconventional reservoirs. Traditional empirical models like Arps decline are inadequate in capturing these complexities, and long-term forecasting is hindered by the challenges posed by 3 phase flow. In response, a new physics-augmented, data-driven forecasting method has been proposed that efficiently captures these complexities.\n The proposed PI-based forecasting (PIBF) method combines data-driven techniques with the physics of propagation of dynamic drainage volume under transient flow conditions observed by unconventional wells for a prolonged period. The model requires only routinely measured inputs such as production rates and wellhead pressure, and efficiently captures the trend shift in gas-to-oil ratio caused by free gas liberation in the near-wellbore region. By using material balance and productivity index models, the proposed approach can forecast well performance and handle changing operational conditions during the well's lifecycle.\n Compared to existing empirical or analytical methods like Arps decline and RTA, the proposed method yields more accurate forecasting results, while still using easily available inputs. Empirical methods like Arps decline have low input requirements but lack physical insights, leading to inaccuracies and inability to handle changing operational conditions. Pure physics-based methods like RTA and reservoir simulation require more input properties that are often difficult to obtain, resulting in a low range of applicability.\n Overall, the proposed method offers a promising alternative to existing methods, effectively combining data-driven techniques with physics-based insights to accurately forecast well performance and handle changing operational conditions in unconventional reservoirs.","PeriodicalId":158776,"journal":{"name":"Day 3 Wed, May 24, 2023","volume":"77 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hybrid Multiphase Rate Forecasting Model in Liquid Wells for Unconventional Reservoirs\",\"authors\":\"Utkarsh Sinha, Hardikkumar Zalavadia, P. Chauhan, S. Sankaran\",\"doi\":\"10.2118/212981-ms\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n The development of shale plays requires accurate forecasting of production rates and expected ultimate recoveries, which is challenging due to the complexities associated with production from hydraulically fractured horizontal wells in unconventional reservoirs. Traditional empirical models like Arps decline are inadequate in capturing these complexities, and long-term forecasting is hindered by the challenges posed by 3 phase flow. In response, a new physics-augmented, data-driven forecasting method has been proposed that efficiently captures these complexities.\\n The proposed PI-based forecasting (PIBF) method combines data-driven techniques with the physics of propagation of dynamic drainage volume under transient flow conditions observed by unconventional wells for a prolonged period. The model requires only routinely measured inputs such as production rates and wellhead pressure, and efficiently captures the trend shift in gas-to-oil ratio caused by free gas liberation in the near-wellbore region. By using material balance and productivity index models, the proposed approach can forecast well performance and handle changing operational conditions during the well's lifecycle.\\n Compared to existing empirical or analytical methods like Arps decline and RTA, the proposed method yields more accurate forecasting results, while still using easily available inputs. Empirical methods like Arps decline have low input requirements but lack physical insights, leading to inaccuracies and inability to handle changing operational conditions. Pure physics-based methods like RTA and reservoir simulation require more input properties that are often difficult to obtain, resulting in a low range of applicability.\\n Overall, the proposed method offers a promising alternative to existing methods, effectively combining data-driven techniques with physics-based insights to accurately forecast well performance and handle changing operational conditions in unconventional reservoirs.\",\"PeriodicalId\":158776,\"journal\":{\"name\":\"Day 3 Wed, May 24, 2023\",\"volume\":\"77 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Day 3 Wed, May 24, 2023\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2118/212981-ms\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 3 Wed, May 24, 2023","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/212981-ms","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

页岩气藏的开发需要准确预测产量和预期最终采收率,由于非常规油藏水力压裂水平井生产的复杂性,这是一项具有挑战性的工作。传统的经验模型(如Arps下降)不足以捕捉这些复杂性,并且长期预测受到三相流带来的挑战的阻碍。为此,提出了一种新的物理增强、数据驱动的预测方法,可以有效地捕捉这些复杂性。所提出的基于pi的预测(PIBF)方法将数据驱动技术与非常规井长时间观测到的瞬态流动条件下动态排水体积传播的物理特性相结合。该模型只需要常规测量的输入,如产量和井口压力,并有效捕获近井区域游离气释放引起的气油比趋势变化。通过使用物料平衡和产能指数模型,该方法可以预测井的性能,并处理井生命周期中不断变化的操作条件。与现有的经验或分析方法(如Arps下降和RTA)相比,该方法在使用容易获得的输入的同时,可以获得更准确的预测结果。经验方法,如Arps下降,输入要求低,但缺乏物理洞察力,导致不准确,无法处理不断变化的操作条件。纯粹基于物理的方法,如RTA和油藏模拟,需要更多难以获得的输入属性,导致适用性范围较低。总的来说,该方法为现有方法提供了一种有希望的替代方法,有效地将数据驱动技术与基于物理的见解相结合,以准确预测非常规油藏的井况,并应对不断变化的作业条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid Multiphase Rate Forecasting Model in Liquid Wells for Unconventional Reservoirs
The development of shale plays requires accurate forecasting of production rates and expected ultimate recoveries, which is challenging due to the complexities associated with production from hydraulically fractured horizontal wells in unconventional reservoirs. Traditional empirical models like Arps decline are inadequate in capturing these complexities, and long-term forecasting is hindered by the challenges posed by 3 phase flow. In response, a new physics-augmented, data-driven forecasting method has been proposed that efficiently captures these complexities. The proposed PI-based forecasting (PIBF) method combines data-driven techniques with the physics of propagation of dynamic drainage volume under transient flow conditions observed by unconventional wells for a prolonged period. The model requires only routinely measured inputs such as production rates and wellhead pressure, and efficiently captures the trend shift in gas-to-oil ratio caused by free gas liberation in the near-wellbore region. By using material balance and productivity index models, the proposed approach can forecast well performance and handle changing operational conditions during the well's lifecycle. Compared to existing empirical or analytical methods like Arps decline and RTA, the proposed method yields more accurate forecasting results, while still using easily available inputs. Empirical methods like Arps decline have low input requirements but lack physical insights, leading to inaccuracies and inability to handle changing operational conditions. Pure physics-based methods like RTA and reservoir simulation require more input properties that are often difficult to obtain, resulting in a low range of applicability. Overall, the proposed method offers a promising alternative to existing methods, effectively combining data-driven techniques with physics-based insights to accurately forecast well performance and handle changing operational conditions in unconventional reservoirs.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信