Application of depth learning in prediction of glutenite reservoir

Abstract

Sedimentary facies of gravel reservoir has the characteristics of multiple sources and short flow. Sand body thickness changes rapidly in plane and space. Model-based multi-attribute regression seismic reservoir prediction method often results in unreasonable inter-well prediction results because of insufficient generalization ability of the model. For this reason, a deep learning reservoir prediction method based on fault blocks is proposed. Within a fault block range (regardless of wells outside the fault block), the seismic attributes extracted from the sliding time window of the isochronous sedimentary surface are used as input, and the sandstone thickness data of the target layer of the well point are used as expected output to train the depth neural network model of the multi-layer perceptor. Then, the trained model is used to predict the sandstone thickness distribution in the well distribution area. The application of actual data in A oilfield shows that the predictive coincidence rate of sandstone thickness in known wells is more than 95%, and the drilling occurrence rate of sandstone in horizontal section after completion of a posterior horizontal well is more than 80%. This method has high prediction accuracy and good generalization ability.