| 1. | Wavelet transform of logging data and its geological significance
测井数据小波变换及其地质意义 |
| 2. | Wireless well - logging data exchange system
无线测井数据交换系统 |
| 3. | Inversion of reservoir parameters with seismic attributes and logging data
应用地震属性与测井数据反演储层参数 |
| 4. | Neural network models for predicating the spatial distribution of reservoir parameters based on seismic and well logging data
测井数据预测储层参数空间分布规律的神经网络模型 |
| 5. | Some normalization methods are discussed in detail , including histogram method , expectation - variance method and trend surface analysis
这里详细介绍了各种测井数据标准化的方法原理，并给出了相应的实例。 |
| 6. | In reservoir description , log data must be normalized to make sure that the same type of log data has same calibration
摘要在油藏描述中必须对测井数据进行标准化处理，其目的是使研究区的所有同类测井数据具有统一的刻度。 |
| 7. | According to the theory and normalization procedure , the normalization software is designed at windows2000 system for precise reservoir description
此外，针对油藏描述中测井数据标准化处理的流程，设计了相应的软件，为精确油藏描述作好了准备。 |
| 8. | We can obtain the eccentric circle ' s center , maximum drift diameter and effective drift diameter by using this method and the data of multi armed caliper log
利用该方法和多臂井径测井数据可得到变形截面上近似偏心圆的圆心、最大通径和有效通经。 |
| 9. | Using gamma ray log , neutron log , density log and sonic log , with the algorithm of fuzzy clustering , we can realize lithology recognition
摘要利用测井数据中的自然伽玛、中子、声波和密度测井曲线所蕴含的岩性信息，用模糊聚类算法实现岩性的自动划分。 |
| 10. | According to some right logging data and the theories of nerve network and fuzzy identify , an accurate reasonable classification model for the reservoir is established ; 2
选择合适的测井数据，应用神经网络理论及模糊识别理论，建立准确、合理的储层分类模型； 2 |
