利用BP人工神经网络建立油气水层解释模型

Building an Oil,Gas and Water Layer Interpretation Model Using BP Artificial Nerve Network

利用BP人工神经网络误差反传播算法,开发了录井神经网络解释软件,通过对焉耆盆地勘探至今的已试油层的原始资料的学习、训练,建立了焉耆盆地神经网络储层解释模型,运用该模型可圆满地解决储层流体类型(凝析油气层、油气层、油层、气层、油水同层、水层和干层)的划分和识别,使得综合利用气测、地化、测井等原始资料识别储层流体类型,实现计算机处理自动化。其精度及解释效果均令人满意,该方法值得推广应用。

Mud logging nerve network interpretation software was developed using BP artificial nerve network′s error backpropagation algorithm.The software provides Yanqi Basin′s nerve network reservoir interpretation model by studying and training the basin′s initial data of tested oil.The model can divide and recognize reservoir′s fluid type(gas-oil condensating reservoir,hydrocarbon reservoir,oil reservoir,gas reservoir,oil-water layer,water layer and dry layer)successfully.It makes the computer automatically identify reservoir′s fluid type using gas logging,geochemical and welllogging′s initial data,the accuracy and interpretation result are satisfying.This method is worthy to be applied to mud logging′s data interpretation.