| 大庆深部致密砂砾岩含气储层产能预测 |
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| 引用本文： | 王祝文,刘菁华,许延清.大庆深部致密砂砾岩含气储层产能预测[J].吉林大学学报(地球科学版),2003,33(4):485-489. |
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| 作者姓名： | 王祝文 刘菁华 许延清 |
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| 作者单位： | 吉林大学,地球探测科学与技术学院,吉林,长春,130026 |
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| 基金项目： | 国家自然科学基金"九五"重大项目和大庆石油管理局联合资助项目(4989419042) |
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| 摘 要： | 气层产能预测是气藏工程研究中用于指导气井以及气田合理生产的重要工作和任务，它在气田整体评价和高效开发进程中具有很强的预见性和主动性。讨论了大庆深部致密砂砾岩含气储层的产能与测井响应之间的关系，探讨了根据测井资料应用人工神经网络技术预测含气储层产能的方法。利用已知气井测试结果和测井资料作为网络的训练样本。根据网络学习训练结果，输入储集层的测井资料等静态参数，可预测该储集层的产能。根据这种关系采用神经网络技术实现了测井对产能的预测评价，从而为大庆深部致密砂砾岩含气储层的开发提供了一定的依据。
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| 关 键 词： | 含气储层 致密砂砾岩 神经网络 产能预测 大庆油田 |
| 文章编号： | 1671-5888(2003)04-0485-05 |
| 修稿时间： | 2003年4月17日 |
GAS PRODUCTIVITY PREDICTION FOR TIGHT SANDY GRAVEL FORMATION IN THE DAQING OILFIELD |
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| Abstract: | The gas productivity prediction is one of the main tasks in the oil/gas development. This paper discussed the potential relationship between the productive capacity and the logging response of a deeply buried, gas-bearing and tight sandy gravel formation in the Daqing oilfield,and probed the method to predict the productivity of the gas-bearing formation employing the neural network technique based on the logging data. The test results of the gas-bearing formation and the corresponding logging data were taken as the neural network training samples. Based on the training result, the static parameters of the logging data such as neutron porosity, density etc. were taken as the input parameters of the network to predict the productive capacity of the gas-bearing formation. Hence, the present paper provides a new parameter or tool for the development design of the deeply buried, gas-bearing and tight sandy gravel formation in the Daqing oilfield,thus has widen the application range of well-logging in oil/gas exploration and exploitation. |
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| Keywords: | tight sandy gravel neural network productivity prediction |
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