人工神经网络在低阻油层识别上的应用

Application of nerve network on oil-bearing formation with low resistivity

低阻油层作为一种非常规储层 ,其含油性受多个因素影响。常规测井解释方法评价低阻油层有很大的困难。人工神经网络具有自适应、自学习、抗干扰能力较强的特点。本文利用BP模型 ,结合低阻油层的电性特征 ,成功地对河南油区下二门油田的低阻油气层进行了识别。

As an unconventional one, low resistivity oil-bearing formation is affected by many factors and common logging interpretation method meets difficulty in evaluating the same formation. However, artificial nerve network features self-adapting, self-learning and strong disturbance resistant. Oil and gasformations are identified successfully with BP model combining the electrical properties of low resistivityoil formation.