Though traditional methods could recognize some facies, e.g. lagoon facies, backshoal facies and foreshoal facies, they couldn't recognize reef facies and shoal facies well. To solve this problem, back propagation...Though traditional methods could recognize some facies, e.g. lagoon facies, backshoal facies and foreshoal facies, they couldn't recognize reef facies and shoal facies well. To solve this problem, back propagation neural network(BP-ANN) and an improved BP-ANN with better stability and suitability, optimized by a particle swarm optimizer(PSO) algorithm(PSO-BP-ANN) were proposed to solve the microfacies' auto discrimination of M formation from the R oil field in Iraq. Fourteen wells with complete core, borehole and log data were chosen as the standard wells and 120 microfacies samples were inferred from these 14 wells. Besides, the average value of gamma, neutron and density logs as well as the sum of squares of deviations of gamma were extracted as key parameters to build log facies(facies from log measurements)-microfacies transforming model. The total 120 log facies samples were divided into 12 kinds of log facies and 6 kinds of microfacies, e.g. lagoon bioclasts micrite limestone microfacies, shoal bioclasts grainstone microfacies, backshoal bioclasts packstone microfacies, foreshoal bioclasts micrite limestone microfacies, shallow continental micrite limestone microfacies and reef limestone microfacies. Furthermore, 68 samples of these 120 log facies samples were chosen as training samples and another 52 samples were gotten as testing samples to test the predicting ability of the discrimination template. Compared with conventional methods, like Bayes stepwise discrimination, both the BP-ANN and PSO-BP-ANN can integrate more log details with a correct rate higher than 85%. Furthermore, PSO-BP-ANN has more simple structure, smaller amount of weight and threshold and less iteration time.展开更多
There are various sand tipples in the natural world. The viewpoint of Yalin is that local disturbances result in laminar instability and in sand-tipple formation, namely, local disturbance^the instability of the lamin...There are various sand tipples in the natural world. The viewpoint of Yalin is that local disturbances result in laminar instability and in sand-tipple formation, namely, local disturbance^the instability of the laminar flow→the formation of sand ripples. Based on this viewpoint, a theoretical model of the resonant triad interaction and its nonlinear interaction with the sediment is established. The purpose of this model is to explain the formation and evolution of the sand-tipple and allow for analysis of the instability of open-channel flow caused by it and sand-tipple hydro-dynamic process. This model will not only pave a road to explore the mechanism of interaction between bed-form and turbulence, but also provide a good base for the study of aeolian sand-tipple formation.展开更多
基金Project(41272137) supported by the National Natural Science Foundation of China
文摘Though traditional methods could recognize some facies, e.g. lagoon facies, backshoal facies and foreshoal facies, they couldn't recognize reef facies and shoal facies well. To solve this problem, back propagation neural network(BP-ANN) and an improved BP-ANN with better stability and suitability, optimized by a particle swarm optimizer(PSO) algorithm(PSO-BP-ANN) were proposed to solve the microfacies' auto discrimination of M formation from the R oil field in Iraq. Fourteen wells with complete core, borehole and log data were chosen as the standard wells and 120 microfacies samples were inferred from these 14 wells. Besides, the average value of gamma, neutron and density logs as well as the sum of squares of deviations of gamma were extracted as key parameters to build log facies(facies from log measurements)-microfacies transforming model. The total 120 log facies samples were divided into 12 kinds of log facies and 6 kinds of microfacies, e.g. lagoon bioclasts micrite limestone microfacies, shoal bioclasts grainstone microfacies, backshoal bioclasts packstone microfacies, foreshoal bioclasts micrite limestone microfacies, shallow continental micrite limestone microfacies and reef limestone microfacies. Furthermore, 68 samples of these 120 log facies samples were chosen as training samples and another 52 samples were gotten as testing samples to test the predicting ability of the discrimination template. Compared with conventional methods, like Bayes stepwise discrimination, both the BP-ANN and PSO-BP-ANN can integrate more log details with a correct rate higher than 85%. Furthermore, PSO-BP-ANN has more simple structure, smaller amount of weight and threshold and less iteration time.
基金supported by the National Natural Science Foundation of China (Grant No. 50979066)the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (Grant No. 51021004)
文摘There are various sand tipples in the natural world. The viewpoint of Yalin is that local disturbances result in laminar instability and in sand-tipple formation, namely, local disturbance^the instability of the laminar flow→the formation of sand ripples. Based on this viewpoint, a theoretical model of the resonant triad interaction and its nonlinear interaction with the sediment is established. The purpose of this model is to explain the formation and evolution of the sand-tipple and allow for analysis of the instability of open-channel flow caused by it and sand-tipple hydro-dynamic process. This model will not only pave a road to explore the mechanism of interaction between bed-form and turbulence, but also provide a good base for the study of aeolian sand-tipple formation.
