Relationship between organic carbon content of shale gas reservoir and logging parameters and its prediction model.

Total organic carbon (TOC) content of shale gas reservoir is an important parameter of shale gas assessment, and how to accurately determine the content of TOC is a key problem of shale gas exploration and development. The author used the Lower Silurian Longmaxi formation in Qianjiang area as the research object. Through the statistical analysis of TOC content testing of shale gas reservoir and drilling and logging data, the log response characteristics of TOC content were analyzed. Furthermore, the four logs which consist of volume density logging(DEN), gamma logging (GR) spontaneous potential logging(SP) and acoustic logging(AC) were selected optimally as the feature vector. Afterwards, the BP neural network prediction model of TOC content was established, the BP neural network algorithm was improved and the TOC content of Lower Silurian Longmaxi formation of two shale gas wells in Qianjiang area were predicted and compared. The results show that the BP neural network model based on logging parameters has strongly approximate nonlinearization, which can reflect the nonlinear relationship between the TOC content of shale gas reservoir and logging parameters. The error between prediction results and measured values is small,and the relative error is less than 10%. [ABSTRACT FROM AUTHOR]