Evaluation of Organic Carbon Logging in the Chang 7 Section of the Yanchang Formation, Huanjiang Area, Ordos Basin

Abstract

During the early stages of shale oil exploration, challenges such as limited coring of source rocks and the discontinuous distribution of measured samples arise. Logging data can be used to quantitatively evaluate source rocks. Organic-rich source rocks typically exhibit characteristics such as high gamma radiation, low density, high sonic lag, high resistivity, and high neutron porosity on logging curves. This paper systematically introduces two quantitative evaluation methods for source rocks based on logging data: the Δlog R method and the BP neural network model. Corresponding prediction models were developed to forecast the organic carbon content of source rocks in the Chang 7 section of the Yanchang Formation in the Huanjiang area of the Ordos Basin. The predicted TOC (total organic carbon) data were copared with measured TOC data. The results indicate that both the ΔlogR model and the BP neural network model are effective for the study area, with the BP neural network model showing significantly better fitting performance than the ΔlogR model. The study also predicts the plane distribution of TOC content in the source rocks of the study area.