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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.