Comprehensive evaluation of gas-bearing properties in ultra-deep basement reservoirs based on an optimizable support vector machine

Ultra-deep basement gas reservoirs represent an important new frontier in oil and gas exploration. However, due to their complex lithology, strong heterogeneity, and severe overlap in logging responses, conventional logging methods face significant limitations in fluid identification. A gas-bearing identification method is proposed for the ultra-deep basement reservoir in the K2 Block based on an optimizable support vector machine. A classification model was constructed using natural gamma ray, sonic transit time, density, compensated neutron, deep and shallow lateral resistivity, and total hydrocarbon as input features, with gas layer, gas-bearing layer, and dry layer as output labels. Combined with cross-validation to optimize the kernel function and penalty parameters, high-precision identification of gas-bearing properties in ultra-deep basement reservoirs was achieved. The accuracy of the model reached 97.5% on the training set and 95.3% on the test set, with AUC values of the ROC curve exceeding 0.97 in both cases. The model shows good interpretation consistency and generalization capability in wells K2, K1-1, K101, and the newly drilled well K2-2, with the selection of perforation intervals showing strong agreement with actual productivity. On this basis, a standardized identification workflow was established, covering sample screening, classification criteria formulation, model construction, comprehensive interpretation, and dynamic validation, providing effective technical support for the logging evaluation of ultra-deep basement gas reservoirs.