| 应用人工智能优化降低核磁共振孔隙度测量误差 |
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| 引用本文： | 周宇,魏国齐,郭和坤.应用人工智能优化降低核磁共振孔隙度测量误差[J].西安石油大学学报(自然科学版),2011,26(5). |
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| 作者姓名： | 周宇 魏国齐 郭和坤 |
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| 作者单位： | 1. 中国科学院渗流流体力学研究所,河北 廊坊,065007
2. 中国石油勘探开发研究院廊坊分院,河北 廊坊,065007 |
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| 基金项目： | 国家重点基础研究发展规划“973”项目“中低丰度天然气藏大面积成藏机理与有效开发的基础研究”(编号:2007CB209500); 国家自然科学基金项目“低渗透气藏储层特征及流体运动渗流机理研究”(编号:10672187) |
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| 摘 要： | 核磁共振测量孔隙度通常偏小,这是因为储层岩石中含有顺磁物质和黏土.为此提出了一种通过采用人工智能算法,根据相关因素对核磁共振测量孔隙度进行校准的新方法.该方法首先根据信息增益的原则,通过数据挖掘找出与核磁孔隙度偏差相关的因素作为神经网络的参数,之后用常规方法测得的孔隙度对神经网络进行训练,并根据实验结果对网络的算法和参数进行优化,最终将实测核磁孔隙度的相对误差从29.35%降低到11.37%.这一结果表明应用人工智能算法能够有效提高核磁共振法测量孔隙度的精度.
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| 关 键 词： | 核磁共振 孔隙度测量 神经网络 数据挖掘 信息增益 |
Improvement of nuclear magnet resource reservoir porosity measuring accuracy by artificial intelligent algorithm |
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| ZHOU Yu,WEI Guo-qi,GUO He-kun.Improvement of nuclear magnet resource reservoir porosity measuring accuracy by artificial intelligent algorithm[J].Journal of Xian Shiyou University,2011,26(5). |
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| Authors: | ZHOU Yu WEI Guo-qi GUO He-kun |
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| Affiliation: | ZHOU Yu1,WEI Guo-qi2,GUO He-kun2(1.Institute of Porous Flow and Fluid Mechanics,Chinese Academy of Sciences,Langfang 065007,Hebei,China,2.Langfang Branch,Research Institute of Petroleum Exploration & Development,PetroChina,China) |
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| Abstract: | The reservoir porosity measured by nuclear magnetic resonance(NMR) is usually less than that measured using regular method,which is because the reservoir rock contains paramagnetic materials and clay.A method decreasing nuclear magnetic resonance porosity measuring error is put forward,which is based on rock's information and using artificial neural network method.Firstly,the factors related to NMR porosity measurement error are found by data mining of a large amount of core sample information based on info... |
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| Keywords: | nuclear magnetic resonance porosity measurement neural network data mining information gain |
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