An integrated convolutional neural network prediction framework for in situ shale oil content based on conventional logging data

The quantification of total organic carbon (TOC) and the free hydrocarbon content (S₁) is crucial for evaluating the shale oil generation and bearing properties of source rocks. This study aimed to enhance the accuracy of TOC and S₁ quantification in shale oil evaluations. The scope encompassed the development of a novel deep learning framework to overcome the limitations of traditional physical and machine learning or deep learning methods. This paper proposes an integrated swarm optimization algorithm–convolutional neural network/machine learning framework. This framework uses a swarm optimization algorithm for the hyperparameter optimization of a convolutional neural network or machine learning framework, utilizing experimental data from core samples preserved in liquid nitrogen alongside well logging data. The application of the proposed framework to the H11 well in the Subei Basin, China, using 110 core samples, demonstrated a superior performance. The results validate the framework's effectiveness in predicting the TOC and S₁ contents at various depths. The proposed framework stands out for its convenient methodology, wide application range and high precision in prediction. These attributes contribute significantly to the field of petroleum engineering and development, offering a novel approach that promises to enhance both the efficiency and accuracy of well logging evaluation.