公开(公告)号：US20240094419A1

公开(公告)日：2024-03-21

申请号：US18341781

申请日：2023-06-27

Applicant: Chengdu University of Technology

Inventor： Chaorong Wu ,  Cheng Liu ,  Kaixing Huang ,  Yong Li ,  Yizhen Li ,  Junxiang Li ,  Yuexiang Hao

IPC: G01V1/30

CPC classification number: G01V1/30 ,  G01V2210/6169

Abstract: A seismic quantitative prediction method for shale total organic carbon (TOC) based on sensitive parameter volumes is as follows. A target stratum for a TOC content to be measured is determined, logging curves with high correlations with TOC contents are analyzed, the logging curves are found as sensitive parameters; sample data are constructed using the sensitive parameters; a radial basis function (RBF) neural network is trained with the sample data as an input and the TOC content at a depth corresponding to the sample data as an output to obtain a RBF neural network prediction model; sensitive parameter volumes are obtained by using the sensitive parameters and post stack three-dimension seismic data to invert; prediction samples are constructed using the sensitive parameter volumes; the predicted samples are input to the RBF neural network prediction model to calculate corresponding TOC values, thereby the TOC content of the target stratum is predicted.