An Improved Small-Sample Method Based on APSO-LSSVM for Gas-Bearing Probability Distribution Prediction From Multicomponent Seismic Data

Abstract

Multicomponent seismic data contain abundant reservoir information and have significant advantages for reservoir prediction. However, due to limited exploration in some areas, the available data samples are limited. Hence, it is important to develop new methods that can identify the complex relationship between oil-gas-bearing property and the multicomponent seismic response with limited samples. The least-squares support vector machine (LSSVM) model is suitable for solving small-sample and optimization problems. However, the parameter optimization of the LSSVM kernel function affects the prediction results. Therefore, a hybrid artificial intelligence model based on adaptive particle swarm optimization (APSO) and LSSVM was developed for multicomponent seismic reservoir prediction. Different kernel functions of the LSSVM model were analyzed to determine which one had the best predictive performance, and this initial model was used for optimization. The problem of the PSO falling into the local minimum was alleviated by adaptively adjusting the inertia weight and velocity coefficients. The kernel function parameters of the LSSVM model were globally searched via APSO, and the APSO-LSSVM model for reservoir distribution prediction was obtained. Finally, the gas-bearing probability distribution in the area with few wells was predicted using the multicomponent seismic data and the APSO-LSSVM model. The results showed that APSO had a faster convergence speed in parameter optimization than PSO. Additionally, the prediction results of the proposed hybrid artificial intelligence model (APSO-LSSVM) were more accurate than those of the LSSVM alone. This model achieved good prediction results with a small number of samples, providing a new method for gas reservoir prediction in areas with insufficient exploration degree.