利用测井资料预测克拉玛依油田八区克上组油层产能

Using log data for predicting oil-gas production capacity of Keshang Formation reservoir in eighth block, Kelamayi oil-field.

基于达西二维产量公式 ,通过研究储层产能的理论公式 ,并分析储层产能的两类主要影响因素 (人为因素和储层因素 ) ,认为在一个油区内各种作业方式等人为因素大致相同的前提下 ,储层产能主要取决于储层的性质。在此基础上 ,本文建立起储层产能与测井数据之间的关系 ,采用人工神经网络技术建立了储层产能预测系统。该系统采用了 5个评价参数 (有效孔隙率、渗透率、含油饱和度、泥质含量和产能系数 )作为输入节点 ,通过人工神经网络 (ANN)模型预测出表示储层动态特征的结果。将本方法用于预测新疆克拉玛依油田八区克上组油层的产能 ,取得了良好的效果 ,从而证实了本方法的有效性。

Starting from 2-D Darcy production rate formula, the paper studied theoretic expression in production capacity of reservoir and analyzed two categories of major effecting factor (artificial factors and reservoir factors). It can consider that in a precondition of that artificial factors such as every operation way are roughly the same in production region, the production capacity of reservoir mainly depends on characters of reservoir. The paper built up the relation between production capacity of reservoir and log data on that basis and adopted artificial neural networks technique in building up predicting system for production capacity of reservoir. Adopting five appraisal parameters (effective porosity, permeability, oil saturation, shale conten and production capacity factor) as a input knot, the system predicted the results presented dinamic character of reservoir through ANN model. The method was used to predict oil-gas production capacity of Keshang Formation reservoir in eighth block of Kelamayi oil-field of Xinjiang autonomous region and good results were obtained, it further proved the effectiveness of the method.