Longchuan Chen

The core of the exploration and development of unconventional oil and gas resources, such as shale oil, lies in the effective identification and scale utilization of the sweet spot. Oil saturation is an important parameter in evaluating the sweet spot. Aiming to solve the many current problems of oil saturation logging evaluations of shale oil reservoirs, this study outlines the research progress of oil saturation logging evaluations of shale oil from the three aspects, namely, the electrical method, non-electrical method, and machine learning, through researching the literature and practical applications. At the same time, several typical saturation models are applied to shale oil reservoirs in the Qingshankou Formation of the Gulong Depression, and applicability analyses are conducted. Lastly, the advantages and disadvantages of each oil saturation calculation model are summarized, and suggestions are given for conducting research using each type of method. This study has certain significance in the selection of oil saturation logging evaluation methods for shale oil reservoirs and provides directions for improvement.

Total organic carbon (TOC) content is a key indicator for determining the hydrocarbon content of shale. The current model for calculating the TOC content of shale is relatively simplistic, the modeling process is cumbersome, and the parameters involved are influenced by subjective factors, which have certain shortcomings. To address this problem, a time-domain convolutional neural network (TCN) model for predicting total organic carbon content based on logging sequence information was established by starting from logging sequence information, conducting logging parameter sensitivity analysis experiments, prioritizing logging-sensitive parameters as model feature vectors, and constructing a TCN network. Meanwhile, to overcome the problem of an insufficient sample size, a five-fold cross-validation method was used to train the TCN model and obtain the weight matrix with the minimum error, and then a shale reservoir TOC content prediction model based on the TCN model was established. The model was applied to evaluate the TOC logging of the Lianggaoshan Formation in the Sichuan Basin, China, and the predicted results were compared with the traditional ΔlogR model. The results indicate that the TCN model predicts the TOC content more accurately than the traditional model, as demonstrated by laboratory tests. This leads to a better application effect. Additionally, the model fully explores the relationship between the logging curve and the total organic carbon content, resulting in improved accuracy of the shale TOC logging evaluation.