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The identification of shale lithology is of great importance for the exploration and development of shale reservoirs. The lithology and mineralogical composition of shale are closely related, but a small number of laboratory core analysis samples are insufficient to evaluate the lithology of the entire formation. In this study, a lithology identification method using conventional logging curves is proposed for the shale stratigraphy of the Qingshankou Formation in the Gulong Depression of the Songliao Basin, northeastern China. First, a mineral pre-training model is constructed using discrete petrophysical experimental data with logging data, and features are generated for the logging data. Second, an adaptive multi-objective swarm crossover optimization method is employed to address the imbalance of logging data. Finally, the model is combined with a Bayesian gradient boosting algorithm for lithology identification. The proposed method demonstrates superior performance to eXtreme Gradient Boosting, Support Vector Machines, Multilayer Perceptron, and Random Forest in terms of accuracy, weight perspective, and macro perspective evaluation indexes. The method has been successfully applied in actual wells, with excellent results. The results indicate that the workflow is a reliable means of shale lithology identification.

The geothermal gradient in the eastern area of Liaoning Province is very low, but hot springs resources are variable. The reason is not clear till now but leads to the fact that a few strong influence factors can cause imbalances in the results of many prediction algorithms. It can be found as a black-box algorithm, deep learning will obtain a more unbalanced result with the fault influence factors. To tackle this issue, the role of preprocessing during the process of profound learning was enhanced and four comparative experiments were carried out. The results show that compared with the unprocessed experiment, the accuracy rate of the experiment with fully processed data increased by 11.9 p.p., and the area under the curve increased by 0.086 (0.796–0.882). This inspires us that even though the deep learning method can achieve high accuracy in the prediction of geological resources, we still need to pay attention to the analysis and pretreatment of data with expertise according to local conditions.

In the process of deep geological structure research, gravity and aeromagnetic anomaly data are often used to invert the distribution law of concealed geological bodies. In order to reveal the boundaries of concealed geological bodies and divide structural divisions, this paper adopts fuzzy control simulated annealing genetic algorithm () to superimpose the gravity and aeromagnetic anomaly data in Benxi-Xiuyan area and classify the patterns, and identify the main geological body boundaries and fractures. Comparing the relationship with known geological maps, the two have a higher degree of matching. Experiments show that the application of SAGAFcm algorithm can quickly identify the boundaries of concealed geological bodies and provide a new means for field geological mapping.