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Rapid evaluation of capillary pressure and relative permeability for oil-water flow in tight sandstone based on a physics-informed neural network

文献类型：期刊论文

作者Ji LL(姬莉莉); Xu, Fengyang; Lin M(林缅); Jiang WB(江文滨); Cao GH(曹高辉); Wu, Songtao; Jiang, Xiaohua
刊名JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY
出版日期2023-08-09
ISSN号2190-0558
关键词Two-phase flow Capillary pressure curve Relative permeability curve Tight sandstone Physics-informed neural network
DOI10.1007/s13202-023-01682-7
英文摘要Efficient and accurate evaluation of capillary pressure and relative permeability of oil-water flow in tight sandstone with limited routinely obtainable parameters is a crucial problem in tight oil reservoir modeling and petroleum engineering. Due to the multiscale pore structure, there is complex nonlinear multiphase flow in tight sandstone. Additionally, wetting behavior caused by mineral components remarkably influences oil-water displacement in multiscale pores. All this makes predicting capillary pressure and relative permeability in tight sandstone extremely difficult. This paper proposes a physics-informed neural network, integrating five important physical models, the improved parallel genetic algorithm (PGA), and the neural network to simulate the two-phase capillary pressure and relative permeability of tight sandstone. To describe the nonlinear multiphase flow and the wettability behavior, five physical models, including the non-Darcy liquid flow rate formula, apparent permeability (AP) formula, and contact angle-capillary pressure relationship, are coupled into the neural network to improve the prediction accuracy. In addition, the input parameters and the structure of the physics-informed neural network are simplified based on analyzing the change rule of the oil-water flow with the main controlling factors, which can also save training time and improve the accuracy of the neural network. To obtain the data for training the coupled neural network, the dataset of tight sandstone in Ordos Basin is constructed with experimentally measured data and various fluid flow properties as constraints. The test results demonstrate that the estimated capillary pressure and relative permeability from the physics-informed neural network are in good agreement with the test ones. Finally, we have compared the physics-informed neural network with the quasi-static pore network model (QSPNM), dynamic pore network model (DPNM), and conventional artificial neural network (ANN). The calculation time of QSPNM and DPNM are hundreds of times longer than that of the physics-informed neural network. The coupled neural network has also performed much better than the conventional ANN. As the heterogeneity of pore spaces in tight sandstone increases, the advantages of the physics-informed neural network over ANN are more prominent. The prediction models generated in this study can estimate the capillary pressure and relative permeability based on only four routine parameters in a few seconds. Therefore, the physics-informed neural network in this paper can provide the potential parameters for large-scale reservoir simulation.
分类号二类/Q1
WOS研究方向Energy & Fuels ; Engineering ; Geology
语种英语
WOS记录号WOS:001044725800002
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences [XDA14010304] ; National Natural Science Foundation of China [42030808, 41872163] ; open fund of Petro China Research Institute of Petroleum Exploration amp ; Development [RIPED-2022-JS-2104]
其他责任者Lin, M (corresponding author), Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China. ; Lin, M (corresponding author), Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100190, Peoples R China. ; Wu, ST (corresponding author), PetroChina, Res Inst Petr Explorat & Dev, Beijing 100083, Peoples R China.
源URL[http://dspace.imech.ac.cn/handle/311007/92608]  
专题力学研究所_流固耦合系统力学重点实验室(2012-)
作者单位1.{Ji, Lili, Lin, Mian, Jiang, Wenbin} Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100190, Peoples R China
2.{Ji, Lili, Lin, Mian, Jiang, Wenbin, Cao, Gaohui} Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China
3.{Xu, Fengyang} China France Bohai Geoserv Co Ltd, Tianjin 300456, Peoples R China
4.{Wu, Songtao, Jiang, Xiaohua} PetroChina, Res Inst Petr Explorat & Dev, Beijing 100083, Peoples R China
推荐引用方式
GB/T 7714		 Ji LL,Xu, Fengyang,Lin M,et al. Rapid evaluation of capillary pressure and relative permeability for oil-water flow in tight sandstone based on a physics-informed neural network[J]. JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY,2023.
APA 姬莉莉.,Xu, Fengyang.,林缅.,江文滨.,曹高辉.,...&Jiang, Xiaohua.(2023).Rapid evaluation of capillary pressure and relative permeability for oil-water flow in tight sandstone based on a physics-informed neural network.JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY.
MLA 姬莉莉,et al."Rapid evaluation of capillary pressure and relative permeability for oil-water flow in tight sandstone based on a physics-informed neural network".JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY (2023).

入库方式： OAI收割

来源：力学研究所

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