LAUSR

The recent years have witnessed a great success of artificial intelligence applications in geological prospecting, so that the traditional manual work, which is time-consuming and labor-intensive, could be accomplished automatically or at least in a human–machine cooperation way. This letter presents a first attempt in proposing an automatic way to predict the cross-well sandstone that plays a crucial role in formation characterization and reservoir exploration. Such a two-stage framework is composed of i) a convolution neural network (CNN)-based coarse prediction module and ii) a geological experience-based error correction (EC) module. Experiments demonstrate that our proposed module can achieve comparable accuracy as experts.