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.

As an unconventional and hidden special oil reservoir, a low-resistance oil reservoir has huge exploration potential. The formation of low-resistance oil reservoirs in the study area is affected by multiple factors, and the electrical characteristics of low-resistance oil reservoirs are not obvious, which makes it difficult to accurately evaluate the oil- and gas-bearing nature of low-resistance reservoirs by traditional saturation evaluation methods. The water saturation evaluation has become the focus and difficulty of the logging interpretation of low-resistance oil reservoirs in the target blocks. Low-resistance oil reservoirs have various reservoir characteristics and genesis mechanisms. Based on this, this paper firstly analyzes the genesis mechanisms of low-resistance oil reservoirs by combining geological, core NMR, X-diffraction, and well logging data. Secondly, on the basis of clarifying the genesis mechanism and the main controlling factors of the low-resistance oil formation, the additional conductivity correction of clay is carried out by combining it with the petroelectricity experiment, and the petroelectricity parameters are determined. Finally, the saturation of the low-resistance oil formation in the study area is quantitatively evaluated using the Simandoux model. The results show that the high immobile water saturation caused by the complex pore structure and the additional electrical conductivity of clay are the main controlling causes of the low-resistance reservoirs in the study area. Compared with the saturation evaluation effect of the W-S model, the evaluation accuracy of the Simandoux model corrected for the additional electrical conductivity of clay is better than that of the W-S model, which is 0.88, the relative error of its computation is 7.2%, and the model constructed can provide quantitative bases for the dredging of the low-resistance reservoirs in the Bohai Basin.

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.

The rock coring of the reservoir in the Bohai A field is difficult. The cores of the target section in the study area are loose, making it difficult to accurately measure the core-bound water saturation. The purpose of this research was to develop and validate a method for calculating a reservoir core-bound water saturation ratio using the cast thin section. First, pepper noise denoising and image enhancement were performed on the thin section by median filtering and gamma variation. Based on this, the enhanced sheet images were thresholded for segmentation by the two-dimensional OTSU algorithm, which automatically picked up the thin section pore-specific parameters. Then, the thin section image was equivalent to a capillary cross-section, while the thin film water fused to the pore surface was observed as bound water. For hydrophilic rocks with a strong homogeneity, the area of thin film water in the pore space of the sheet was divided by the total area of the pore space, which produced the bound water saturation. Next, the theoretical relationship between the film water thickness and the critical pore throat radius was derived based on the Young–Laplace equation. The bound water saturation of the rock was calculated by combining the pore perimeter and the area that was automatically picked up from the thin film for a given critical pore throat radius of the rock. Finally, 22 images of thin sections of sparse sandstone from the coring well section of the study area were image processed using the new method proposed in this paper, and the bound water saturation was calculated. The calculated results were compared with 22 NMR-bound water saturations and 11 semi-permeable baffle plate-bound water saturations in the same layer section. The results showed that the bound water saturation values calculated by the three methods produced consistent trends with absolute errors within 5%. The calculated results confirm the reliability of the method proposed in this paper. This method can effectively avoid the problem of the inaccurate results of core experiments due to the easy damage of sparse sandstone and provides a new idea for the accurate determination of the bound water saturation of sparse sandstone.