Gas-Bearing Reservoir Prediction Using k-nearest neighbor Based on Nonlinear Directional Dimension Reduction

In this study, a k-nearest neighbor (kNN) method based on nonlinear directional dimension reduction is applied to gas-bearing reservoir prediction. The kNN method can select the most relevant training samples to establish a local model according to feature similarities. However, the kNN method cannot extract gas-sensitive attributes and faces dimension problems. The features important to gas-bearing reservoir prediction could not be the main features of the samples. Thus, linear dimension reduction methods, such as principal component analysis, fail to extract relevant features. We thus implemented dimension reduction using a fully connected artificial neural network (ANN) with proper architecture. This not only increased the separability of the samples but also maintained the samples' inherent distribution characteristics. Moreover, using the kNN to classify samples after the ANN dimension reduction is also equivalent to replacing the deep structure of the ANN, which is considered to have a linear classification function. When applied to actual data, our method extracted gas-bearing sensitive features from seismic data to a certain extent. The prediction results can characterize gas-bearing reservoirs accurately in a limited scope.