用神经网络和地质统计学综合多元信息进行储层预测

Reservoir prediction with integrated information based on artificial neural network technology and geostatistics

由于实际地下地质地球物理条件的复杂性和地震等资料品质所限,相关问题的解决较为困难。在研究中如何将多元信息结合起来是实现这一目标的可行途径。遵循这一思路,在D气田地震储层预测中,针对研究区储层地质、地球物理特点,尝试用神经网络和地质统计学作为不同信息的融合平台,并将二者结合起来,充分地将多种地震属性和测井资料相结合,有效地减少了储层预测的多解性,提高了储层预测精度,达到了良好的应用效果。

How to eliminate mistiness and improve the precision of seismic data interpretation is an everlasting topic in reservoir prediction. However, it is rather difficult to solve the problem due to the complexity of actual geological and geophysical conditions and low quality of seismic data. Recent study shows that integration of information from different sources is an effective way to deal with the issue. Guided by this understanding, we tried to combine artificial neural network technologies and geostatistics in reservoir prediction of the D gas field. By doing so, we realized the integration of multiple seismic attributes with logging data, and effectively reduced the mistiness and improved the precision of prediction.