Identification of Carbonate Cave Reservoirs Based on Variational Bayesian Principal Component Analysis

In recent years, significant advancements have been achieved in the exploration of oil and gas reserves within carbonate rock formations, particularly with respect to the considerable resources found in deep Ordovician fault-controlled karst fracture-cave reservoirs. Accurately identifying such reservoirs using effective geophysical methods is crucial, but it is often challenging due to the low signal-to-noise ratio (SNR) and strong background reflections shielding of raw seismic data. To fully extract the information of carbonate reservoirs contained in the seismic data and enhance interpretation accuracy, we innovatively employ the variational Bayesian principal component analysis (VBPCA) technique to perform background modeling on the raw seismic data, aiming to effectively isolate the bead-like reflections of reservoirs from interfering signals. Subsequently, we conduct attribute analysis on the processed seismic data and optimize the sweetness attribute to identify cave reservoirs. The identified reservoirs exhibit complete shapes with clear boundaries, providing an intuitive depiction of their locations. In comparison to traditional principal component analysis (PCA) and probabilistic PCA (PPCA), VBPCA offers several advantages, including automatic determination of the number of principal components, eliminating the inconvenience of manual settings, more effective separation of reservoir reflections from interfering reflections, and greater robustness to noise. Testing on synthetic seismic records and actual data from an oilfield in northern China has validated the feasibility and effectiveness of the proposed approach for identifying carbonate karst cave reservoirs.