多井条件下进行测井神经网络储层参数计算

CALCULATION OF RESERVOIR PARAMETERS BY THE NEURAL NETWORK MODEL WITH THE LOGGING DATA OF MULTIPLE WELLS

唐萍

TANG Ping

中国石化, 石油勘探开发研究院, 北京, 100083

Research Institute of Exploration and Production, SINOPEC, Beijing 100083, China

神经网络在测井计算储层参数中被广泛应用并获得良好的应用效果,但在具有多口井岩心资料控制条件下,建立统一的数学模型就显得很重要,这对于提高不同钻井之间计算结果的可对比性和井间储层参数预测的精度具有重要的意义。该研究在对测井资料进行编辑、标准化、归一化及深度漂移校正的基础上,建立了分层段多井统一的测井储层参数计算BP神经网络模型。经实际资料验证,模型的预测效果良好。

The neural network has been widely used in reservoir parameter calculation with logging data and has got good effects. But under the control of logging data from multiple wells, it is crucial to construct a unified mathematical model, which helps to promote the contrast of calculating outputs among all the wells and in turn to promote the accuracy of interwell prediction. Based on the edition, standardization, normalization and depth correction of logging data, this research constructed a unified BP neural network model for each sand group. Compared with real samples, the predicting effect was good.

国家自然科学基金资助(40072043).

https://doi.org/10.11781/sysydz200304413