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基于现代神经网络数理统计新技术，提出一种简易可行的ＢＰ神经网络三维估值模型，以此可根据研究区域的已知资料对其未知点处的储层参数值进行高分辨率的预测；采用立体等值线图可视化技术来实现该模型估值的直观图形显示，充分揭示三维地质体某种特征参数的纵横向变化规律．最后成功地处理了Ｓ盆地中部气田马家沟组地层压力数据，结果表明，该法是一种有效的高分辨率估值技术，在油气藏描述中值得推广使用．
Based on the new technology of modern neural network mathematical statistics, a simple and feasible three-dimensional BP neural network estimation model is proposed to predict the reservoir parameters at unknown points according to the known data of the study area. The visualization of the model estimation is realized by using the stereo contour map visualization technique to fully reveal the vertical and horizontal variation of certain characteristic parameters of the three-dimensional geological body. Finally, the formation pressure data of Majiagou Formation in the gas field of the Central S Basin have been successfully processed. The results show that this method is an effective high-resolution estimation technique and should be widely used in reservoir description.