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
The biodegradability evaluation of petrochemical wastewater is vital for regulating the petrochemical wastewater treatment process.Nevertheless,the essential datasets derived by instruments with different sampling sca...The biodegradability evaluation of petrochemical wastewater is vital for regulating the petrochemical wastewater treatment process.Nevertheless,the essential datasets derived by instruments with different sampling scales are characterized by multiple time scales,making it challenging for the existing data-driven biodegradability evaluation methods to achieve feasible results.In this paper,an intelligent evaluation method is proposed based on multiple time-scale analyses to ensure realtime and accurate biodegradability evaluation of the petrochemical wastewater treatment process.Firstly,a multiple time-scale reconfiguration method is introduced to regularize the datasets consistently by regulating the time-series characteristics of the collected variables.Moreover,missing data for large time-scale variables are supplemented by linear interpolation.Secondly,a multi-scale feature extraction algorithm based on partial least squares is designed to obtain biodegradability feature variables and remove noise and redundant information.Thirdly,an intelligent evaluation model based on a dynamic fuzzy min-max neural network is established to realize the classification of biodegradability.Finally,the proposed evaluation method is applied to the practical petrochemical wastewater treatment process.The experimental results demonstrate that the proposed method can provide real-time and accurate evaluation of the petrochemical wastewater biodegradability.展开更多
微博实体链接是把微博中给定的指称链接到知识库的过程,广泛应用于信息抽取、自动问答等自然语言处理任务(Natural language processing,NLP).由于微博内容简短,传统长文本实体链接的算法并不能很好地用于微博实体链接任务.以往研究大...微博实体链接是把微博中给定的指称链接到知识库的过程,广泛应用于信息抽取、自动问答等自然语言处理任务(Natural language processing,NLP).由于微博内容简短,传统长文本实体链接的算法并不能很好地用于微博实体链接任务.以往研究大都基于实体指称及其上下文构建模型进行消歧,难以识别具有相似词汇和句法特征的候选实体.本文充分利用指称和候选实体本身所含有的语义信息,提出在词向量层面对任务进行抽象建模,并设计一种基于词向量语义分类的微博实体链接方法.首先通过神经网络训练词向量模板,然后通过实体聚类获得类别标签作为特征,再通过多分类模型预测目标实体的主题类别来完成实体消歧.在NLPCC2014公开评测数据集上的实验结果表明,本文方法的准确率和召回率均高于此前已报道的最佳结果,特别是实体链接准确率有显著提升.展开更多
基金Supported by the National Science and Technology Major Project(2017ZX05063-005)Science and Technology Development Project of PetroChina Research Institute of Petroleum Exploration and Development(YGJ2019-12-04)。
文摘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.
基金supported by the National Key Research and Development Project(Grant No.2018YFC1900800-5)the National Natural Science Foundation of China(Grant Nos.61890930-5,61622301,61903010,62021003,62103012)Beijing Nova Program(Grant No.20240484694)。
文摘The biodegradability evaluation of petrochemical wastewater is vital for regulating the petrochemical wastewater treatment process.Nevertheless,the essential datasets derived by instruments with different sampling scales are characterized by multiple time scales,making it challenging for the existing data-driven biodegradability evaluation methods to achieve feasible results.In this paper,an intelligent evaluation method is proposed based on multiple time-scale analyses to ensure realtime and accurate biodegradability evaluation of the petrochemical wastewater treatment process.Firstly,a multiple time-scale reconfiguration method is introduced to regularize the datasets consistently by regulating the time-series characteristics of the collected variables.Moreover,missing data for large time-scale variables are supplemented by linear interpolation.Secondly,a multi-scale feature extraction algorithm based on partial least squares is designed to obtain biodegradability feature variables and remove noise and redundant information.Thirdly,an intelligent evaluation model based on a dynamic fuzzy min-max neural network is established to realize the classification of biodegradability.Finally,the proposed evaluation method is applied to the practical petrochemical wastewater treatment process.The experimental results demonstrate that the proposed method can provide real-time and accurate evaluation of the petrochemical wastewater biodegradability.
文摘微博实体链接是把微博中给定的指称链接到知识库的过程,广泛应用于信息抽取、自动问答等自然语言处理任务(Natural language processing,NLP).由于微博内容简短,传统长文本实体链接的算法并不能很好地用于微博实体链接任务.以往研究大都基于实体指称及其上下文构建模型进行消歧,难以识别具有相似词汇和句法特征的候选实体.本文充分利用指称和候选实体本身所含有的语义信息,提出在词向量层面对任务进行抽象建模,并设计一种基于词向量语义分类的微博实体链接方法.首先通过神经网络训练词向量模板,然后通过实体聚类获得类别标签作为特征,再通过多分类模型预测目标实体的主题类别来完成实体消歧.在NLPCC2014公开评测数据集上的实验结果表明,本文方法的准确率和召回率均高于此前已报道的最佳结果,特别是实体链接准确率有显著提升.
