Machine learning-based grayscale analyses for lithofacies identification of the Shahejie formation, Bohai Bay Basin, China

Abstract

It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs. To address the problem of low resolution in logging curves, this study establishes a grayscale-phase model based on high-resolution grayscale curves using clustering analysis algorithms for shale lithofacies identification, working with the Shahejie Formation, Bohai Bay Basin, China. The grayscale phase is defined as the sum of absolute grayscale and relative amplitude as well as their features. The absolute grayscale is the absolute magnitude of the gray values and is utilized for evaluating the material composition (mineral composition + total organic carbon) of shale, while the relative amplitude is the difference between adjacent gray values and is used to identify the shale structure type. The research results show that the grayscale phase model can identify shale lithofacies well, and the accuracy and applicability of this model were verified by the fitting relationship between absolute grayscale and shale mineral composition, as well as corresponding relationships between relative amplitudes and laminae development in shales. Four lithofacies are identified in the target layer of the study area: massive mixed shale, laminated mixed shale, massive calcareous shale and laminated calcareous shale. This method can not only effectively characterize the material composition of shale, but also numerically characterize the development degree of shale laminae, and solve the problem that difficult to identify millimeter-scale laminae based on logging curves, which can provide technical support for shale lithofacies identification, sweet spot evaluation and prediction of complex continental lacustrine basins.