Research on logging interpretation standards for reservoir thickness of the S reservoir formation

Studies have shown that the S reservoir formation is widely developed within the study area and holds considerable potential in reserve resources. Currently, there is a limited understanding of the S reservoir formation, and there is a lack of interpretation standards for its effective thickness. Starting with different logging series and core analysis data, this paper selects sensitive logging curves. To address the issue of the lack of sensitive curves in older logging series, an artificial intelligence BP neural network model is used for logging curve reconstruction. Finally, a standard suitable for the reservoir thickness of the S reservoir formation is established.