In order to perform large scale numerical simulation of wave propagation in 3D heterogeneous multiscale viscoelastic media, Finite Difference technique and its parallel implementation based on domain decomposition is ...In order to perform large scale numerical simulation of wave propagation in 3D heterogeneous multiscale viscoelastic media, Finite Difference technique and its parallel implementation based on domain decomposition is used. A couple of typical statements of borehole geophysics are dealt with—sonic log and cross well measurements. Both of them are essentially multiscales, which claims to take into account heterogeneities of very different sizes in order to provide reliable results of simulations. Locally refined spatial grids help us to avoid the use of redundantly tiny grid cells in a target area, but cause some troubles with uniform load of Processor Units involved in computations. We present results of scalability tests together with results of numerical simulations for both statements performed for some realistic models.展开更多
Tool waves, also named collar waves, propagating along the drill collars in acoustic logging while drilling （ALWD）, strongly interfere with the needed P- and S-waves of a penetrated formation, which is a key issue i...Tool waves, also named collar waves, propagating along the drill collars in acoustic logging while drilling （ALWD）, strongly interfere with the needed P- and S-waves of a penetrated formation, which is a key issue in picking up formation P- and S-wave velocities. Previous studies on physical insulation for the collar waves designed on the collar between the source and the receiver sections did not bring to a satisfactory solution. In this paper, we investigate the propagation features of collar waves in different models. It is confirmed that there exists an indirect collar wave in the synthetic full waves due to the coupling between the drill collar and the borehole, even there is a perfect isolator between the source and the receiver. The direct collar waves propagating all along the tool and the indirect ones produced by echoes from the borehole wall are summarized as the generalized collar waves. Further analyses show that the indirect collar waves could be relatively strong in the full wave data. This is why the collar waves cannot be eliminated with satisfactory effect in many cases by designing the physical isolators carved on the tool.展开更多
In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the...In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the conventional back propagation （BP） model, an improved BP model was proposed, with main modifications of back propagation of error, self-adapting algorithm, and activation function, also a prediction program was developed. The improved BP model was successfully applied to predicting the lithology of formations to be drilled in the Kela-2 gas field.展开更多
A reliable and effective model for reservoir physical property prediction is a key to reservoir characterization and management.At present,using well logging data to estimate reservoir physical parameters is an import...A reliable and effective model for reservoir physical property prediction is a key to reservoir characterization and management.At present,using well logging data to estimate reservoir physical parameters is an important means for reservoir evaluation.Based on the characteristics of large quantity and complexity of estimating process,we have attempted to design a nonlinear back propagation neural network model optimized by genetic algorithm(BPNNGA)for reservoir porosity prediction.This model is with the advantages of self-learning and self-adaption of back propagation neural network(BPNN),structural parameters optimizing and global searching optimal solution of genetic algorithm(GA).The model is applied to the Chang 8 oil group tight sandstone of Yanchang Formation in southwestern Ordos Basin.According to the correlations between well logging data and measured core porosity data,5 well logging curves(gamma ray,deep induction,density,acoustic,and compensated neutron)are selected as the input neurons while the measured core porosity is selected as the output neurons.The number of hidden layer neurons is defined as 20 by the method of multiple calibrating optimizations.Modeling results demonstrate that the average relative error of the model output is 10.77%,indicating the excellent predicting effect of the model.The predicting results of the model are compared with the predicting results of conventional multivariate stepwise regression algorithm,and BPNN model.The average relative errors of the above models are 12.83%,12.9%,and 13.47%,respectively.Results show that the predicting results of the BPNNGA model are more accurate than that of the other two,and BPNNGA is a more applicable method to estimate the reservoir porosity parameters in the study area.展开更多
文摘In order to perform large scale numerical simulation of wave propagation in 3D heterogeneous multiscale viscoelastic media, Finite Difference technique and its parallel implementation based on domain decomposition is used. A couple of typical statements of borehole geophysics are dealt with—sonic log and cross well measurements. Both of them are essentially multiscales, which claims to take into account heterogeneities of very different sizes in order to provide reliable results of simulations. Locally refined spatial grids help us to avoid the use of redundantly tiny grid cells in a target area, but cause some troubles with uniform load of Processor Units involved in computations. We present results of scalability tests together with results of numerical simulations for both statements performed for some realistic models.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11134011 and 11374322)the Foresight Research Project,Institute of Acoustics,Chinese Academy of Sciences
文摘Tool waves, also named collar waves, propagating along the drill collars in acoustic logging while drilling （ALWD）, strongly interfere with the needed P- and S-waves of a penetrated formation, which is a key issue in picking up formation P- and S-wave velocities. Previous studies on physical insulation for the collar waves designed on the collar between the source and the receiver sections did not bring to a satisfactory solution. In this paper, we investigate the propagation features of collar waves in different models. It is confirmed that there exists an indirect collar wave in the synthetic full waves due to the coupling between the drill collar and the borehole, even there is a perfect isolator between the source and the receiver. The direct collar waves propagating all along the tool and the indirect ones produced by echoes from the borehole wall are summarized as the generalized collar waves. Further analyses show that the indirect collar waves could be relatively strong in the full wave data. This is why the collar waves cannot be eliminated with satisfactory effect in many cases by designing the physical isolators carved on the tool.
文摘In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the conventional back propagation （BP） model, an improved BP model was proposed, with main modifications of back propagation of error, self-adapting algorithm, and activation function, also a prediction program was developed. The improved BP model was successfully applied to predicting the lithology of formations to be drilled in the Kela-2 gas field.
基金supported by the National Natural Science Foundation of China(No.41002045)。
文摘A reliable and effective model for reservoir physical property prediction is a key to reservoir characterization and management.At present,using well logging data to estimate reservoir physical parameters is an important means for reservoir evaluation.Based on the characteristics of large quantity and complexity of estimating process,we have attempted to design a nonlinear back propagation neural network model optimized by genetic algorithm(BPNNGA)for reservoir porosity prediction.This model is with the advantages of self-learning and self-adaption of back propagation neural network(BPNN),structural parameters optimizing and global searching optimal solution of genetic algorithm(GA).The model is applied to the Chang 8 oil group tight sandstone of Yanchang Formation in southwestern Ordos Basin.According to the correlations between well logging data and measured core porosity data,5 well logging curves(gamma ray,deep induction,density,acoustic,and compensated neutron)are selected as the input neurons while the measured core porosity is selected as the output neurons.The number of hidden layer neurons is defined as 20 by the method of multiple calibrating optimizations.Modeling results demonstrate that the average relative error of the model output is 10.77%,indicating the excellent predicting effect of the model.The predicting results of the model are compared with the predicting results of conventional multivariate stepwise regression algorithm,and BPNN model.The average relative errors of the above models are 12.83%,12.9%,and 13.47%,respectively.Results show that the predicting results of the BPNNGA model are more accurate than that of the other two,and BPNNGA is a more applicable method to estimate the reservoir porosity parameters in the study area.
