基于BP神经网络算法识别苏里格气田致密砂岩储层岩性

Identification of Lithology in Tight Sandstone Reservoir in Sulige Gas Field Based on BP Neural Net Algorithm

以薄片鉴定资料为准,从筛选敏感测井参数入手,优选了对岩性敏感的自然伽马和光电吸收截面指数作为BP神经网络输入端信息进行网络训练和学习,对测井数据进行了标准化处理,以消除由于测井系列以及仪器型号的不同引起的刻度误差。应用BP神经网络法对苏里格气田目的层段复杂岩性进行识别,其结果与岩心录井岩性符合度较高,平均符合率达84.48%。采用该方法对致密砂岩气藏储层岩性的识别,可以有效地为后续沉积相等方面的研究提供资料基础。

Based on thin rock slices and different sensitive logging parameters of the lithology, natural gamma ray and photoelectric absorption cross-section index were preferred as the BP neural network input information, which are sensitive to lithology to conduct network training and learning. The standardized logging datum were used to eliminate error of graduation caused by logging series and different models. The results proved that the BP neural network is effective to identify the objective interval complex lithology in Sulige gas field, and the recognition results are higher coincidence rate than those from core and logging lithology, and which coincidence rate is 84. 480%. Therefore, using the method to recognize reservoir lithology of tight sandstone gas reservoirs will provide a strong support to sedimentary and other aspects in subsequent research.