Reducing Simulation Time in a Huff-And-Puff Gas Injection Project in Complex Shale Reservoirs: Sequence-Based Proxy Multi-Porosity Reservoir Simulator

Cristhian Aranguren, Carlos Rodríguez Araque, Santiago Cuervo, A. Fragoso, R. Aguilera
{"title":"Reducing Simulation Time in a Huff-And-Puff Gas Injection Project in Complex Shale Reservoirs: Sequence-Based Proxy Multi-Porosity Reservoir Simulator","authors":"Cristhian Aranguren, Carlos Rodríguez Araque, Santiago Cuervo, A. Fragoso, R. Aguilera","doi":"10.2118/212821-ms","DOIUrl":null,"url":null,"abstract":"\n The objective of this project is to explore cutting-edge sequence-based machine learning models commonly used in language processing to reproduce a multi-porosity reservoir simulator. The proposed method integrates advanced techniques to significantly reduce the numerical simulation time and improve the decision-making process for Huff and Puff (H-n-P) gas injection optimization in shale reservoirs. The proposed approach follows three crucial steps to predict an output sequence given an input sequence: 1) the simulation results should be validated against actual data, 2) train and validate a machine learning model using simulation results from either commercial or in-house numerical simulators, 3) exhaustive exploration of hyperparameter tuning and selection of machine learning techniques, such as sequence-to-sequence (Seq2Seq), Luong attention and ConvLSTM. The proxy model considers as input variables well control parameters such as injection and production periods, number of cycles and gas injection rates to estimate the proxy model results.\n The multi-porosity proxy reservoir simulation model is a complementary tool that integrates numerical simulation and data-driven techniques. Although tuning the model typically demands significant time, it can speed up the simulation time up to 20,000X allowing for generating hundreds or even thousands of scenarios at the expense of accepting a reduction in the accuracy of the results in a matter of minutes. One of the most notable findings is that considering a small training dataset, the proxy model can reproduce the capabilities for predicting oil production in complex low and ultra-low permeability reservoirs with significantly reduced error, relative to the multi-porosity reservoir simulator. Finally, the possibility of reproducing a considerable number of scenarios in minutes opens the door to exploring different well control configurations such as injection and production periods, number of cycles and gas injection rates. The novelty of the proxy multi-porosity reservoir simulator is to notably accelerate the numerical simulation time by using techniques capable of solving sequence learning problems in which the output is dependent on previous outputs.","PeriodicalId":437231,"journal":{"name":"Day 1 Wed, March 15, 2023","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 1 Wed, March 15, 2023","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/212821-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

Abstract

The objective of this project is to explore cutting-edge sequence-based machine learning models commonly used in language processing to reproduce a multi-porosity reservoir simulator. The proposed method integrates advanced techniques to significantly reduce the numerical simulation time and improve the decision-making process for Huff and Puff (H-n-P) gas injection optimization in shale reservoirs. The proposed approach follows three crucial steps to predict an output sequence given an input sequence: 1) the simulation results should be validated against actual data, 2) train and validate a machine learning model using simulation results from either commercial or in-house numerical simulators, 3) exhaustive exploration of hyperparameter tuning and selection of machine learning techniques, such as sequence-to-sequence (Seq2Seq), Luong attention and ConvLSTM. The proxy model considers as input variables well control parameters such as injection and production periods, number of cycles and gas injection rates to estimate the proxy model results. The multi-porosity proxy reservoir simulation model is a complementary tool that integrates numerical simulation and data-driven techniques. Although tuning the model typically demands significant time, it can speed up the simulation time up to 20,000X allowing for generating hundreds or even thousands of scenarios at the expense of accepting a reduction in the accuracy of the results in a matter of minutes. One of the most notable findings is that considering a small training dataset, the proxy model can reproduce the capabilities for predicting oil production in complex low and ultra-low permeability reservoirs with significantly reduced error, relative to the multi-porosity reservoir simulator. Finally, the possibility of reproducing a considerable number of scenarios in minutes opens the door to exploring different well control configurations such as injection and production periods, number of cycles and gas injection rates. The novelty of the proxy multi-porosity reservoir simulator is to notably accelerate the numerical simulation time by using techniques capable of solving sequence learning problems in which the output is dependent on previous outputs.
减少复杂页岩储层吞吐注气项目的模拟时间:基于序列的代理多孔隙度油藏模拟器
该项目的目标是探索语言处理中常用的基于序列的尖端机器学习模型,以重现多孔隙度油藏模拟器。该方法集成了先进的技术,显著缩短了数值模拟时间,改善了页岩储层H-n-P注气优化决策过程。提出的方法遵循三个关键步骤来预测给定输入序列的输出序列:1)模拟结果应针对实际数据进行验证,2)使用商业或内部数值模拟器的模拟结果训练和验证机器学习模型,3)详尽探索超参数调整和机器学习技术的选择,如序列到序列(Seq2Seq), Luong注意和ConvLSTM。代理模型将注采周期、循环次数、注气速率等井控参数作为输入变量来估计代理模型的结果。多孔隙度代理油藏模拟模型是数值模拟与数据驱动技术相结合的补充工具。虽然调整模型通常需要大量的时间,但它可以将模拟时间加快到20,000倍,允许生成数百甚至数千个场景,代价是在几分钟内接受结果准确性的降低。最值得注意的发现之一是,相对于多孔隙度油藏模拟器,考虑到较小的训练数据集,代理模型可以再现复杂低渗透和超低渗透油藏的产量预测能力,且误差显著降低。最后,在几分钟内重现大量场景的可能性,为探索不同的井控配置(如注入和生产周期、循环次数和注气速率)打开了大门。代理多孔隙度油藏模拟器的新颖之处在于,通过使用能够解决序列学习问题的技术,显著加快了数值模拟时间,而序列学习问题的输出依赖于先前的输出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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:604180095
Book学术官方微信