石化技术
石化技術
석화기술
PETROCHEMICAL INDUSTRY TECHNOLOGY
2015年
3期
154-154,162
,共2页
神经网络%油气水层%录井解释
神經網絡%油氣水層%錄井解釋
신경망락%유기수층%록정해석
Neural network%oil,gas and water layers%interpretation of mud logging
基于BP神经网络的算法原理，本文通过对渤海油田某工区录井、测井、测试数据等解释结论的学习和训练，构建了录井油气水层神经网络解释模型，运用该模型可进行储层流体性质的识别和划分，解释符合率达到了80%以上。
基于BP神經網絡的算法原理，本文通過對渤海油田某工區錄井、測井、測試數據等解釋結論的學習和訓練，構建瞭錄井油氣水層神經網絡解釋模型，運用該模型可進行儲層流體性質的識彆和劃分，解釋符閤率達到瞭80%以上。
기우BP신경망락적산법원리，본문통과대발해유전모공구록정、측정、측시수거등해석결론적학습화훈련，구건료록정유기수층신경망락해석모형，운용해모형가진행저층류체성질적식별화화분，해석부합솔체도료80%이상。
Based on the principle of BP neural network algorithm, interpretation results of mud logging, well logging and testing in Bohai oilfield are analyzed with the method of learning and training in this paper. In the study, the author built the model of BP neural network for the interpretation of oil, gas and water layers with mud logging data. By using this model, properties of reservoir fluids can be identified and divided. Finally, the interpretation coincidence rate of this model is proved to reach above 80%.
