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A smart productivity evaluation method for shale gas wells based on 3D fractal fracture network model 被引量:1
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作者 WEI Yunsheng WANG Junlei +4 位作者 YU Wei QI Yadong MIAO Jijun YUAN He LIU Chuxi 《Petroleum Exploration and Development》 CSCD 2021年第4期911-922,共12页
The generation method of three-dimensional fractal discrete fracture network(FDFN)based on multiplicative cascade process was developed.The complex multi-scale fracture system in shale after fracturing was characteriz... The generation method of three-dimensional fractal discrete fracture network(FDFN)based on multiplicative cascade process was developed.The complex multi-scale fracture system in shale after fracturing was characterized by coupling the artificial fracture model and the natural fracture model.Based on an assisted history matching(AHM)using multiple-proxy-based Markov chain Monte Carlo algorithm(MCMC),an embedded discrete fracture modeling(EDFM)incorporated with reservoir simulator was used to predict productivity of shale gas well.When using the natural fracture generation method,the distribution of natural fracture network can be controlled by fractal parameters,and the natural fracture network generated coupling with artificial fractures can characterize the complex system of different-scale fractures in shale after fracturing.The EDFM,with fewer grids and less computation time consumption,can characterize the attributes of natural fractures and artificial fractures flexibly,and simulate the details of mass transfer between matrix cells and fractures while reducing computation significantly.The combination of AMH and EDFM can lower the uncertainty of reservoir and fracture parameters,and realize effective inversion of key reservoir and fracture parameters and the productivity forecast of shale gas wells.Application demonstrates the results from the proposed productivity prediction model integrating FDFN,EDFM and AHM have high credibility. 展开更多
关键词 fractal discrete fracture network multiplicative cascade process embedded discrete fracture model intelligent history matching reservoir parameter inversion shale gas smart productivity evaluation
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低渗气藏压裂井稳定产能预测方法研究 被引量:2
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作者 苏建政 《钻采工艺》 CAS 北大核心 2008年第5期90-92,102,共4页
通过分析压裂气井稳定产能与其影响因素之间的相关性,应用目前流行的BP人工神经网络方法,建立了压后气井稳态产能预测模型,并且在Matlab软件平台上对网络模型实现。根据现场收集的近30口气井的压裂施工数据和压后产能数据,对网络进行训... 通过分析压裂气井稳定产能与其影响因素之间的相关性,应用目前流行的BP人工神经网络方法,建立了压后气井稳态产能预测模型,并且在Matlab软件平台上对网络模型实现。根据现场收集的近30口气井的压裂施工数据和压后产能数据,对网络进行训练,并将训练好的网络用于同区块的压裂井稳定产能预测分析。结果表明,与常规方法相比,该方法不需要复杂数值模拟计算,预测精度可以指导现场生产,为气田在区块开发过程中压裂气井稳定产能评价提供了一种分析方法。 展开更多
关键词 压裂气井 神经网络 产能评价
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