This study investigates the effects of selected petrophysical properties on predicting flowing well bottomhole pressure.To efficiently situate the essence of this investigation,genetic,imperialist competitive and whal...This study investigates the effects of selected petrophysical properties on predicting flowing well bottomhole pressure.To efficiently situate the essence of this investigation,genetic,imperialist competitive and whale optimization algorithms were used in predicting the bottomhole pressure of a reservoir using production data and some selected petrophysical properties as independent input variables.A total of 15,633 data sets were collected from Volvo field in Norway,and after screening the data,a total of 9161 data sets were used to develop apt computational intelligence models.The data were randomly divided into three different groups:training,validation,and testing data.Two case scenarios were considered in this study.The first scenario involved the prediction of flowing bottomhole pressure using only eleven independent variables,while the second scenario bothered on the prediction of the same flowing bottomhole pressure using the same independent variables and two selected petrophysical properties(porosity and permeability).Each of the two scenarios involved as implied in the first scenario,the use of three(3)heuristic search optimizers to determine optimal model architectures.The optimizers were allowed to choose the optimal number of layers(between 1 and 10),the optimal number of nodal points(between 10 and 100)for each layer and the optimal learning rate required per task/operation.the results,showed that the models were able to learn the problems well with the learning rate fixed from 0.001 to 0.0001,although this became successively slower as the leaning rate decreased.With the chosen model configuration,the results suggest that a moderate learning rate of 0.0001 results in good model performance on the trained and tested data sets.Comparing the three heuristic search optimizers based on minimum MSE,RMSE,MAE and highest coefficient of determination(R^(2))for the actual and predicted values,shows that the imperialist competitive algorithm optimizer predicted the flowing bottomhole pressure most accurately展开更多
A global optimization algorithm （GOA） for parallel Chien search circuit in Reed-Solomon （RS） （255,239） decoder is presented. By finding out the common modulo 2 additions within groups of Galois field （GF） mult...A global optimization algorithm （GOA） for parallel Chien search circuit in Reed-Solomon （RS） （255,239） decoder is presented. By finding out the common modulo 2 additions within groups of Galois field （GF） multipliers and pre-computing the common items, the GOA can reduce the number of XOR gates efficiently and thus reduce the circuit area. Different from other local optimization algorithms, the GOA is a global one. When there are more than one maximum matches at a time, the best match choice in the GOA has the least impact on the final result by only choosing the pair with the smallest relational value instead of choosing a pair randomly. The results show that the area of parallel Chien search circuits can be reduced by 51% compared to the direct implementation when the group-based GOA is used for GF multipliers and by 26% if applying the GOA to GF multipliers separately. This optimization scheme can be widely used in general parallel architecture in which many GF multipliers are involved.展开更多
A dense seismic network was installed in the capital region of China in recent years,which makes it possible to resolve the focal mechanisms of small earthquakes. We gathered large earthquake focal mechanisms from the...A dense seismic network was installed in the capital region of China in recent years,which makes it possible to resolve the focal mechanisms of small earthquakes. We gathered large earthquake focal mechanisms from the last fifty years and moderate or small earthquake focal mechanisms from between 2002 and 2004,and calculated the present tectonic stress field of the capital region by the grid search method, which weighs different sized earthquakes and can improve the accuracy of the stress field inversion. The analysis of inversion results of different sub-regions shows that the azinuth of the maximum principal compressive stress axis is NE43°- 86° in the Beijing-Zhangjiakou-Datong area,NE38°-86° in the Tangshan area,and NE79°- 81° in the Xingtai area. Inversion results of this paper are similar to previous results,which proves the correctness of the approach. As revealed by the results,the stress field of the capital region is characterized by overall consistency and sub-regional differences. This study provides reference for earthquake mechanism explanation and geodynamics research.展开更多
After research on the motion estimation algorithm in video coding, a motion estimator algorithm with 1/4 pixel ac- curacy is implemented based on fie/d-programmable gate array （FPGA）. The motion estimation algorithm...After research on the motion estimation algorithm in video coding, a motion estimator algorithm with 1/4 pixel ac- curacy is implemented based on fie/d-programmable gate array （FPGA）. The motion estimation algorithm module is made up of the 1[4 pixel interpolation module with serial input and parallel output, the three step search module and the block match- ing module, which can use relatively less Wiener filters for interpolation operation. Experiment results show that the hard- ware design has less consumption of the logical resource, higher stability and lower power consumption.展开更多
文摘This study investigates the effects of selected petrophysical properties on predicting flowing well bottomhole pressure.To efficiently situate the essence of this investigation,genetic,imperialist competitive and whale optimization algorithms were used in predicting the bottomhole pressure of a reservoir using production data and some selected petrophysical properties as independent input variables.A total of 15,633 data sets were collected from Volvo field in Norway,and after screening the data,a total of 9161 data sets were used to develop apt computational intelligence models.The data were randomly divided into three different groups:training,validation,and testing data.Two case scenarios were considered in this study.The first scenario involved the prediction of flowing bottomhole pressure using only eleven independent variables,while the second scenario bothered on the prediction of the same flowing bottomhole pressure using the same independent variables and two selected petrophysical properties(porosity and permeability).Each of the two scenarios involved as implied in the first scenario,the use of three(3)heuristic search optimizers to determine optimal model architectures.The optimizers were allowed to choose the optimal number of layers(between 1 and 10),the optimal number of nodal points(between 10 and 100)for each layer and the optimal learning rate required per task/operation.the results,showed that the models were able to learn the problems well with the learning rate fixed from 0.001 to 0.0001,although this became successively slower as the leaning rate decreased.With the chosen model configuration,the results suggest that a moderate learning rate of 0.0001 results in good model performance on the trained and tested data sets.Comparing the three heuristic search optimizers based on minimum MSE,RMSE,MAE and highest coefficient of determination(R^(2))for the actual and predicted values,shows that the imperialist competitive algorithm optimizer predicted the flowing bottomhole pressure most accurately
文摘A global optimization algorithm （GOA） for parallel Chien search circuit in Reed-Solomon （RS） （255,239） decoder is presented. By finding out the common modulo 2 additions within groups of Galois field （GF） multipliers and pre-computing the common items, the GOA can reduce the number of XOR gates efficiently and thus reduce the circuit area. Different from other local optimization algorithms, the GOA is a global one. When there are more than one maximum matches at a time, the best match choice in the GOA has the least impact on the final result by only choosing the pair with the smallest relational value instead of choosing a pair randomly. The results show that the area of parallel Chien search circuits can be reduced by 51% compared to the direct implementation when the group-based GOA is used for GF multipliers and by 26% if applying the GOA to GF multipliers separately. This optimization scheme can be widely used in general parallel architecture in which many GF multipliers are involved.
基金sponsored by the Special Fund of Fundamental Scientific Research Operating Expenses for Higher School of Central Government(Projects for creation teams ZY20110101)the Special Fund for the Earthquake Scientific Research of China(201208009)National Natural Science Foundation of China(41074072)
文摘A dense seismic network was installed in the capital region of China in recent years,which makes it possible to resolve the focal mechanisms of small earthquakes. We gathered large earthquake focal mechanisms from the last fifty years and moderate or small earthquake focal mechanisms from between 2002 and 2004,and calculated the present tectonic stress field of the capital region by the grid search method, which weighs different sized earthquakes and can improve the accuracy of the stress field inversion. The analysis of inversion results of different sub-regions shows that the azinuth of the maximum principal compressive stress axis is NE43°- 86° in the Beijing-Zhangjiakou-Datong area,NE38°-86° in the Tangshan area,and NE79°- 81° in the Xingtai area. Inversion results of this paper are similar to previous results,which proves the correctness of the approach. As revealed by the results,the stress field of the capital region is characterized by overall consistency and sub-regional differences. This study provides reference for earthquake mechanism explanation and geodynamics research.
基金The 8th Graduate Technology Fund of North University of China(No.201111)
文摘After research on the motion estimation algorithm in video coding, a motion estimator algorithm with 1/4 pixel ac- curacy is implemented based on fie/d-programmable gate array （FPGA）. The motion estimation algorithm module is made up of the 1[4 pixel interpolation module with serial input and parallel output, the three step search module and the block match- ing module, which can use relatively less Wiener filters for interpolation operation. Experiment results show that the hard- ware design has less consumption of the logical resource, higher stability and lower power consumption.
