Based on seismic and logging data,taking the downthrow fault nose of Binhai fault in Qikou Sag as the object of study,we analyzed fault characteristics,sand body distribution,fault-sand combinations and hydrocarbon ac...Based on seismic and logging data,taking the downthrow fault nose of Binhai fault in Qikou Sag as the object of study,we analyzed fault characteristics,sand body distribution,fault-sand combinations and hydrocarbon accumulation to reveal the hydrocarbon enrichment law in the fault-rich area of fault depression lake basin.The results show that the Binhai Cenozoic fault nose is characterized by east-west zoning,the main part of the western fault segment is simple in structure,whereas the broom-shaped faults in the eastern segment are complex in structure,including several groups of faults.The difference of fault evolution controls the spatial distribution of sand bodies.The sand bodies are in continuous large pieces in the downthrow fault trough belt along the Gangdong Fault in the middle segment of the fault nose,forming consequent fault-sand combination;whereas the fault activity period of the eastern part of the fault nose was later,and the sand bodies controlled by paleogeomorphology are distributed in multi-phase north-south finger-shaped pattern,forming vertical fault-sand combination pattern matching with the fault.The configuration between faults and sand bodies,and oil sources and caprocks determine the vertical conductivity,plane distribution and vertical distribution of oil and gas.Two oil and gas accumulation modes,i.e.single main fault hydrocarbon supply-fault sand consequent matching-oil accumulation in multi-layers stereoscopically and fault system transportation-fault sand vertical matching-oil accumulation in banded overlapping layers occur in the middle and eastern segments of the fault nose respectively,and they control the difference of oil and gas distribution and enrichment degree in the Binhai fault nose.展开更多
光伏阵列通常被安装在恶劣的室外环境中,因此在运行过程中易发生故障。为了准确识别光伏阵列的故障类型,提出沙猫群优化支持向量机(sand cat swarm optimization support vector machine,SCSO-SVM)用于光伏组件故障识别,且对比支持向量...光伏阵列通常被安装在恶劣的室外环境中,因此在运行过程中易发生故障。为了准确识别光伏阵列的故障类型,提出沙猫群优化支持向量机(sand cat swarm optimization support vector machine,SCSO-SVM)用于光伏组件故障识别,且对比支持向量机(support vector machine,SVM)、粒子群优化支持向量机(particle swarm optimized support vector machine,PSO-SVM)、遗传优化支持向量机(genetic optimized support vector machine,GA-SVM)、麻雀优化支持向量机(sparrow optimized support vector machine,SSA-SVM)、灰狼优化支持向量机(gray wolf optimized support vector machine,GWO-SVM)和鲸鱼优化支持向量机(whale optimized support vector machine,WOA-SVM)算法。首先,六种SVM混合算法都克服了SVM诊断结果易受参数初始值影响的缺点,识别精度相较传统SVM算法都有所提升,但是识别时间都增加。其次,7种算法中SCSO-SVM识别效果最好,克服了SVM易受参数初始值的影响,相较SVM识别精度提高了约9.4594%;是因为更能有效找到SVM惩罚因子和核函数参数。然后,对于同一种算法而言,算法的识别精度是随输入特征减少而降低的,是因为输入特征越少,越不能有效表征光伏组件在不同故障类型下的输出属性。但算法的识别时间却不是随输入特征减少而减短。所以选取合适的输入特征才能兼顾算法的故障识别准确率和效率。最后,发现七种算法的识别效果依赖于数据集的影响。原因可能是各个算法参数选择过多导致泛化性有差异,且依赖参数初始值选择。展开更多
基金Supported by the China National Science and Technology Major Project(2016ZX05006).
文摘Based on seismic and logging data,taking the downthrow fault nose of Binhai fault in Qikou Sag as the object of study,we analyzed fault characteristics,sand body distribution,fault-sand combinations and hydrocarbon accumulation to reveal the hydrocarbon enrichment law in the fault-rich area of fault depression lake basin.The results show that the Binhai Cenozoic fault nose is characterized by east-west zoning,the main part of the western fault segment is simple in structure,whereas the broom-shaped faults in the eastern segment are complex in structure,including several groups of faults.The difference of fault evolution controls the spatial distribution of sand bodies.The sand bodies are in continuous large pieces in the downthrow fault trough belt along the Gangdong Fault in the middle segment of the fault nose,forming consequent fault-sand combination;whereas the fault activity period of the eastern part of the fault nose was later,and the sand bodies controlled by paleogeomorphology are distributed in multi-phase north-south finger-shaped pattern,forming vertical fault-sand combination pattern matching with the fault.The configuration between faults and sand bodies,and oil sources and caprocks determine the vertical conductivity,plane distribution and vertical distribution of oil and gas.Two oil and gas accumulation modes,i.e.single main fault hydrocarbon supply-fault sand consequent matching-oil accumulation in multi-layers stereoscopically and fault system transportation-fault sand vertical matching-oil accumulation in banded overlapping layers occur in the middle and eastern segments of the fault nose respectively,and they control the difference of oil and gas distribution and enrichment degree in the Binhai fault nose.
文摘光伏阵列通常被安装在恶劣的室外环境中,因此在运行过程中易发生故障。为了准确识别光伏阵列的故障类型,提出沙猫群优化支持向量机(sand cat swarm optimization support vector machine,SCSO-SVM)用于光伏组件故障识别,且对比支持向量机(support vector machine,SVM)、粒子群优化支持向量机(particle swarm optimized support vector machine,PSO-SVM)、遗传优化支持向量机(genetic optimized support vector machine,GA-SVM)、麻雀优化支持向量机(sparrow optimized support vector machine,SSA-SVM)、灰狼优化支持向量机(gray wolf optimized support vector machine,GWO-SVM)和鲸鱼优化支持向量机(whale optimized support vector machine,WOA-SVM)算法。首先,六种SVM混合算法都克服了SVM诊断结果易受参数初始值影响的缺点,识别精度相较传统SVM算法都有所提升,但是识别时间都增加。其次,7种算法中SCSO-SVM识别效果最好,克服了SVM易受参数初始值的影响,相较SVM识别精度提高了约9.4594%;是因为更能有效找到SVM惩罚因子和核函数参数。然后,对于同一种算法而言,算法的识别精度是随输入特征减少而降低的,是因为输入特征越少,越不能有效表征光伏组件在不同故障类型下的输出属性。但算法的识别时间却不是随输入特征减少而减短。所以选取合适的输入特征才能兼顾算法的故障识别准确率和效率。最后,发现七种算法的识别效果依赖于数据集的影响。原因可能是各个算法参数选择过多导致泛化性有差异,且依赖参数初始值选择。
