基于数据驱动的地下储气库地层压力预测
TE822%TP181; 地层压力是制定注采井工作制度和监测储气库运行的重要参数,鉴于数值模拟认识地层压力涉及到复杂的地质建模和高质量的历史拟合,提出一种数据驱动的储气库地层压力预测方法.引入注采气量比重数值加权最优规整路径筛选压力监测井,采用3种机器学习算法建立监督学习地层压力预测模型,即极端梯度提升(extreme gradient boosting,XGBoost)、支持向量回归(support vector regression,SVR)和长短期记忆网络(long short-term memory network,LSTM).实验结果发现,3种预测模型的预测性能从高到低排序为SVR、...
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| Published in | 深圳大学学报（理工版） Vol. 40; no. 3; pp. 353 - 360 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
中国石化人工智能技术联合研发中心,辽宁大连116045%中石化(大连)石油化工研究院有限公司,辽宁大连116045
01.05.2023
中石化(大连)石油化工研究院有限公司,辽宁大连116045 |
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| Online Access | Get full text |
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| Abstract | TE822%TP181; 地层压力是制定注采井工作制度和监测储气库运行的重要参数,鉴于数值模拟认识地层压力涉及到复杂的地质建模和高质量的历史拟合,提出一种数据驱动的储气库地层压力预测方法.引入注采气量比重数值加权最优规整路径筛选压力监测井,采用3种机器学习算法建立监督学习地层压力预测模型,即极端梯度提升(extreme gradient boosting,XGBoost)、支持向量回归(support vector regression,SVR)和长短期记忆网络(long short-term memory network,LSTM).实验结果发现,3种预测模型的预测性能从高到低排序为SVR、XGBoost和LSTM,其中,SVR模型的预测性能最稳定;引入注采气量比重数值筛选压力监测井,提升了预测模型的预测性能.研究表明,依靠纯数据驱动方法,将常规地面监测数据直接用来解释为地下储气库的地层压力,非常适合地下储气库的现场应用. |
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| AbstractList | TE822%TP181; 地层压力是制定注采井工作制度和监测储气库运行的重要参数,鉴于数值模拟认识地层压力涉及到复杂的地质建模和高质量的历史拟合,提出一种数据驱动的储气库地层压力预测方法.引入注采气量比重数值加权最优规整路径筛选压力监测井,采用3种机器学习算法建立监督学习地层压力预测模型,即极端梯度提升(extreme gradient boosting,XGBoost)、支持向量回归(support vector regression,SVR)和长短期记忆网络(long short-term memory network,LSTM).实验结果发现,3种预测模型的预测性能从高到低排序为SVR、XGBoost和LSTM,其中,SVR模型的预测性能最稳定;引入注采气量比重数值筛选压力监测井,提升了预测模型的预测性能.研究表明,依靠纯数据驱动方法,将常规地面监测数据直接用来解释为地下储气库的地层压力,非常适合地下储气库的现场应用. |
| Author | 王晓霖 朱洪翔 傅钰江 李遵照 隋顾磊 |
| AuthorAffiliation | 中石化(大连)石油化工研究院有限公司,辽宁大连116045;中国石化人工智能技术联合研发中心,辽宁大连116045%中石化(大连)石油化工研究院有限公司,辽宁大连116045 |
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| Author_FL | WANG Xiaolin LI Zunzhao ZHU Hongxiang SUI Gulei FU Yujiang |
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| DOI | 10.3724/SP.J.1249.2023.03353 |
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| DocumentTitle_FL | Prediction of formation pressure in underground gas storage based on data-driven method |
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| Keywords | 数据驱动 地层压力 压力监测井 石油与天然气工程 预测性能 预测模型 地下储气库 |
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| Title | 基于数据驱动的地下储气库地层压力预测 |
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