Buiding data-driven models using machine learning methods has gradually become a common approach for studying reservoir parameters.Among these methods,deep learning methods are highly effective.From the perspective of...Buiding data-driven models using machine learning methods has gradually become a common approach for studying reservoir parameters.Among these methods,deep learning methods are highly effective.From the perspective of multi-task learning,this paper uses six types of logging data—acoustic logging(AC),gamma ray(GR),compensated neutron porosity(CNL),density(DEN),deep and shallow lateral resistivity(LLD)and shallow lateral resistivity(LLS)—that are inputs and three reservoir parameters that are outputs to build a porosity saturation permeability network(PSP-Net)that can predict porosity,saturation,and permeability values simultaneously.These logging data are obtained from 108 training wells in a medium₋low permeability oilfield block in the western district of China.PSP-Net method adopts a serial structure to realize transfer learning of reservoir-parameter characteristics.Compared with other existing methods at the stage of academic exploration to simulating industrial applications,the proposed method overcomes the disadvantages inherent in single-task learning reservoir-parameter prediction models,including easily overfitting and heavy model-training workload.Additionally,the proposed method demonstrates good anti-overfitting and generalization capabilities,integrating professional knowledge and experience.In 37 test wells,compared with the existing method,the proposed method exhibited an average error reduction of 10.44%,27.79%,and 28.83%from porosity,saturation,permeability calculation.The prediction and actual permeabilities are within one order of magnitude.The training on PSP-Net are simpler and more convenient than other single-task learning methods discussed in this paper.Furthermore,the findings of this paper can help in the re-examination of old oilfield wells and the completion of logging data.展开更多
At present,coal mine fires were forecasted with some temperature,smog,CO,CO_2,etc,however,this method can't meet the requirements for safe production of coalmines in monitoring accuracy and validity.Overcoming the...At present,coal mine fires were forecasted with some temperature,smog,CO,CO_2,etc,however,this method can't meet the requirements for safe production of coalmines in monitoring accuracy and validity.Overcoming these problems of foregone moni-toring methods,using multi-parameters which include fire image,smog,CO,CO_2,O_2,etc,the paper put forward a synthetical analysis monitor with advanced technology of neuralnetwork.The research and application of this method has significance in theory and prac-tical value for coal mine fire forecast.展开更多
Due to continuous process scaling, process, voltage, and temperature （PVT） parameter variations have become one of the most problematic issues in circuit design. The resulting correlations among performance metrics ...Due to continuous process scaling, process, voltage, and temperature （PVT） parameter variations have become one of the most problematic issues in circuit design. The resulting correlations among performance metrics lead to a significant parametric yield loss. Previous algorithms on parametric yield prediction are limited to predicting a single-parametric yield or performing balanced optimization for several single-parametric yields. Consequently, these methods fail to predict the multiparametric yield that optimizes multiple performance metrics simultaneously, which may result in significant accuracy loss. In this paper we suggest an efficient multi-parametric yield prediction framework, in which multiple performance metrics are considered as simultaneous constraint conditions for parametric yield prediction, to maintain the correlations among metrics. First, the framework models the performance metrics in terms of PVT parameter variations by using the adaptive elastic net （AEN） method. Then the parametric yield for a single performance metric can be predicted through the computation of the cumulative distribution function （CDF） based on the multiplication theorem and the Markov chain Monte Carlo （MCMC） method. Finally, a copula-based parametric yield prediction procedure has been developed to solve the multi-parametric yield prediction problem, and to generate an accurate yield estimate. Experimental results demonstrate that the proposed multi-parametric yield prediction framework is able to provide the designer with either an accurate value for parametric yield under specific performance limits, or a multi-parametric yield surface under all ranges of performance limits.展开更多
文摘Buiding data-driven models using machine learning methods has gradually become a common approach for studying reservoir parameters.Among these methods,deep learning methods are highly effective.From the perspective of multi-task learning,this paper uses six types of logging data—acoustic logging(AC),gamma ray(GR),compensated neutron porosity(CNL),density(DEN),deep and shallow lateral resistivity(LLD)and shallow lateral resistivity(LLS)—that are inputs and three reservoir parameters that are outputs to build a porosity saturation permeability network(PSP-Net)that can predict porosity,saturation,and permeability values simultaneously.These logging data are obtained from 108 training wells in a medium₋low permeability oilfield block in the western district of China.PSP-Net method adopts a serial structure to realize transfer learning of reservoir-parameter characteristics.Compared with other existing methods at the stage of academic exploration to simulating industrial applications,the proposed method overcomes the disadvantages inherent in single-task learning reservoir-parameter prediction models,including easily overfitting and heavy model-training workload.Additionally,the proposed method demonstrates good anti-overfitting and generalization capabilities,integrating professional knowledge and experience.In 37 test wells,compared with the existing method,the proposed method exhibited an average error reduction of 10.44%,27.79%,and 28.83%from porosity,saturation,permeability calculation.The prediction and actual permeabilities are within one order of magnitude.The training on PSP-Net are simpler and more convenient than other single-task learning methods discussed in this paper.Furthermore,the findings of this paper can help in the re-examination of old oilfield wells and the completion of logging data.
基金Supported by Special Funded Project on PhD Subject for Colleges(20050290010)
文摘At present,coal mine fires were forecasted with some temperature,smog,CO,CO_2,etc,however,this method can't meet the requirements for safe production of coalmines in monitoring accuracy and validity.Overcoming these problems of foregone moni-toring methods,using multi-parameters which include fire image,smog,CO,CO_2,O_2,etc,the paper put forward a synthetical analysis monitor with advanced technology of neuralnetwork.The research and application of this method has significance in theory and prac-tical value for coal mine fire forecast.
基金Project supposed by the Natural Science Foundation of Jiangsu Province （Nos. BK20161072, BK20150785, and BK20130877） and the National Natural Science Foundation of China （Nos. 61502234 and 71301081）
文摘Due to continuous process scaling, process, voltage, and temperature （PVT） parameter variations have become one of the most problematic issues in circuit design. The resulting correlations among performance metrics lead to a significant parametric yield loss. Previous algorithms on parametric yield prediction are limited to predicting a single-parametric yield or performing balanced optimization for several single-parametric yields. Consequently, these methods fail to predict the multiparametric yield that optimizes multiple performance metrics simultaneously, which may result in significant accuracy loss. In this paper we suggest an efficient multi-parametric yield prediction framework, in which multiple performance metrics are considered as simultaneous constraint conditions for parametric yield prediction, to maintain the correlations among metrics. First, the framework models the performance metrics in terms of PVT parameter variations by using the adaptive elastic net （AEN） method. Then the parametric yield for a single performance metric can be predicted through the computation of the cumulative distribution function （CDF） based on the multiplication theorem and the Markov chain Monte Carlo （MCMC） method. Finally, a copula-based parametric yield prediction procedure has been developed to solve the multi-parametric yield prediction problem, and to generate an accurate yield estimate. Experimental results demonstrate that the proposed multi-parametric yield prediction framework is able to provide the designer with either an accurate value for parametric yield under specific performance limits, or a multi-parametric yield surface under all ranges of performance limits.
