$Calibrate complex fracture model for subsurface flow based on Bayesian formulation$
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Calibrate complex fracture model for subsurface flow based on Bayesian formulation

Abstract In practical development of unconventional reservoirs, fracture networks are a highly conductive transport media for subsurface fluid flow. Therefore, it is crucial to clearly determine the fracture properties used in production forecast. However, it is different to calibrate the properties...

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Autores principales:	Li-Ming Zhang, Ji Qi, Kai Zhang, Li-Xin Li, Xiao-Ming Zhang, Hai-Yang Wu, Miguel Tome Chipecane, Jun Yao
Formato:	article
Lenguaje:	EN
Publicado:	KeAi Communications Co., Ltd. 2019
Materias:	Complex fracture system Inverse progress Bayesian inverse Model calibration Science Q Petrology QE420-499
Acceso en línea:	https://doaj.org/article/661ae39ed3f843b88369b4df09521978
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spelling	oai:doaj.org-article:661ae39ed3f843b88369b4df095219782021-12-02T08:24:15ZCalibrate complex fracture model for subsurface flow based on Bayesian formulation10.1007/s12182-019-00357-51672-51071995-8226https://doaj.org/article/661ae39ed3f843b88369b4df095219782019-09-01T00:00:00Zhttp://link.springer.com/article/10.1007/s12182-019-00357-5https://doaj.org/toc/1672-5107https://doaj.org/toc/1995-8226Abstract In practical development of unconventional reservoirs, fracture networks are a highly conductive transport media for subsurface fluid flow. Therefore, it is crucial to clearly determine the fracture properties used in production forecast. However, it is different to calibrate the properties of fracture networks because it is an inverse problem with multi-patterns and high-complexity of fracture distribution and inherent defect of multiplicity of solution. In this paper, in order to solve the problem, the complex fracture model is divided into two sub-systems, namely “Pattern A” and “Pattern B.” In addition, the generation method is grouped into two categories. Firstly, we construct each sub-system based on the probability density function of the fracture properties. Secondly, we recombine the sub-systems into an integral complex fracture system. Based on the generation mechanism, the estimation of the complex fracture from dynamic performance and observation data can be solved as an inverse problem. In this study, the Bayesian formulation is used to quantify the uncertainty of fracture properties. To minimize observation data misfit immediately as it occurs, we optimize the updated properties by a simultaneous perturbation stochastic algorithm which requires only two measurements of the loss function. In numerical experiments, we firstly visualize that small-scale fractures significantly contribute to the flow simulation. Then, we demonstrate the suitability and effectiveness of the Bayesian formulation for calibrating the complex fracture model in the following simulation.Li-Ming ZhangJi QiKai ZhangLi-Xin LiXiao-Ming ZhangHai-Yang WuMiguel Tome ChipecaneJun YaoKeAi Communications Co., Ltd.articleComplex fracture systemInverse progressBayesian inverseModel calibrationScienceQPetrologyQE420-499ENPetroleum Science, Vol 16, Iss 5, Pp 1105-1120 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Complex fracture system Inverse progress Bayesian inverse Model calibration Science Q Petrology QE420-499
spellingShingle	Complex fracture system Inverse progress Bayesian inverse Model calibration Science Q Petrology QE420-499 Li-Ming Zhang Ji Qi Kai Zhang Li-Xin Li Xiao-Ming Zhang Hai-Yang Wu Miguel Tome Chipecane Jun Yao Calibrate complex fracture model for subsurface flow based on Bayesian formulation
description	Abstract In practical development of unconventional reservoirs, fracture networks are a highly conductive transport media for subsurface fluid flow. Therefore, it is crucial to clearly determine the fracture properties used in production forecast. However, it is different to calibrate the properties of fracture networks because it is an inverse problem with multi-patterns and high-complexity of fracture distribution and inherent defect of multiplicity of solution. In this paper, in order to solve the problem, the complex fracture model is divided into two sub-systems, namely “Pattern A” and “Pattern B.” In addition, the generation method is grouped into two categories. Firstly, we construct each sub-system based on the probability density function of the fracture properties. Secondly, we recombine the sub-systems into an integral complex fracture system. Based on the generation mechanism, the estimation of the complex fracture from dynamic performance and observation data can be solved as an inverse problem. In this study, the Bayesian formulation is used to quantify the uncertainty of fracture properties. To minimize observation data misfit immediately as it occurs, we optimize the updated properties by a simultaneous perturbation stochastic algorithm which requires only two measurements of the loss function. In numerical experiments, we firstly visualize that small-scale fractures significantly contribute to the flow simulation. Then, we demonstrate the suitability and effectiveness of the Bayesian formulation for calibrating the complex fracture model in the following simulation.
format	article
author	Li-Ming Zhang Ji Qi Kai Zhang Li-Xin Li Xiao-Ming Zhang Hai-Yang Wu Miguel Tome Chipecane Jun Yao
author_facet	Li-Ming Zhang Ji Qi Kai Zhang Li-Xin Li Xiao-Ming Zhang Hai-Yang Wu Miguel Tome Chipecane Jun Yao
author_sort	Li-Ming Zhang
title	Calibrate complex fracture model for subsurface flow based on Bayesian formulation
title_short	Calibrate complex fracture model for subsurface flow based on Bayesian formulation
title_full	Calibrate complex fracture model for subsurface flow based on Bayesian formulation
title_fullStr	Calibrate complex fracture model for subsurface flow based on Bayesian formulation
title_full_unstemmed	Calibrate complex fracture model for subsurface flow based on Bayesian formulation
title_sort	calibrate complex fracture model for subsurface flow based on bayesian formulation
publisher	KeAi Communications Co., Ltd.
publishDate	2019
url	https://doaj.org/article/661ae39ed3f843b88369b4df09521978
work_keys_str_mv	AT limingzhang calibratecomplexfracturemodelforsubsurfaceflowbasedonbayesianformulation AT jiqi calibratecomplexfracturemodelforsubsurfaceflowbasedonbayesianformulation AT kaizhang calibratecomplexfracturemodelforsubsurfaceflowbasedonbayesianformulation AT lixinli calibratecomplexfracturemodelforsubsurfaceflowbasedonbayesianformulation AT xiaomingzhang calibratecomplexfracturemodelforsubsurfaceflowbasedonbayesianformulation AT haiyangwu calibratecomplexfracturemodelforsubsurfaceflowbasedonbayesianformulation AT migueltomechipecane calibratecomplexfracturemodelforsubsurfaceflowbasedonbayesianformulation AT junyao calibratecomplexfracturemodelforsubsurfaceflowbasedonbayesianformulation
_version_	1718398547055869952

Calibrate complex fracture model for subsurface flow based on Bayesian formulation

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