Abstract:

The identification and classification of shale lithofacies are crucial for both theoretical understanding and practical applications in shale gas exploration and exploitation. This study focuses on the shale of the second member of the Paleogene Funing Formation in the Qintong Sag, Subei Basin, using core samples from a typical drilling well, Well-Qinye-1. The research involves whole rock/clay X-ray diffraction analysis on these core samples and employs a previously developed three-terminal diagram of shale mineral components to categorize the types present in this area. Additionally, a BP neural network method optimized by the ASO（Atom Search Optimization） algorithm was utilized to perform data mining on logging information. This process aimed to establish a prediction model for the relative content of clay minerals, siliceous minerals, and carbonate minerals, achieving quantitative characterization of shale mineral content through natural gamma ray spectrometry. Ultimately, the model was applied to predict lithology and identify lithofacies in the second member of Well-Qinye-1 and Well-Shaduo-1. The identification results closely aligned with the data measured from the samples, demonstrating high consistency. This study provides an economical, rapid, and efficient method for predicting shale lithofacies and main mineral components. It also offers a foundational approach for identifying well facies in scenarios where coring and direct testing data are unavailable.

Key words: Qintong Sag, the second member of Funing Formation, logging curve, shale oil and gas, lithofacies identification, neural network